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              <p class="caption" role="heading"><span class="caption-text">Getting Started</span></p>
<ul>
<li class="toctree-l1"><a class="reference internal" href="../../../getting_started/installation.html">Installation</a></li>
<li class="toctree-l1"><a class="reference internal" href="../../../getting_started/jetpack.html">Torch-TensorRT in JetPack</a></li>
<li class="toctree-l1"><a class="reference internal" href="../../../getting_started/quick_start.html">Quick Start</a></li>
<li class="toctree-l1"><a class="reference internal" href="../../../getting_started/capture_and_replay.html">Introduction</a></li>
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<p class="caption" role="heading"><span class="caption-text">User Guide</span></p>
<ul>
<li class="toctree-l1"><a class="reference internal" href="../../../user_guide/torch_tensorrt_explained.html">Torch-TensorRT Explained</a></li>
<li class="toctree-l1"><a class="reference internal" href="../../../user_guide/dynamic_shapes.html">Dynamic shapes with Torch-TensorRT</a></li>
<li class="toctree-l1"><a class="reference internal" href="../../../user_guide/saving_models.html">Saving models compiled with Torch-TensorRT</a></li>
<li class="toctree-l1"><a class="reference internal" href="../../../user_guide/runtime.html">Deploying Torch-TensorRT Programs</a></li>
<li class="toctree-l1"><a class="reference internal" href="../../../user_guide/using_dla.html">DLA</a></li>
<li class="toctree-l1"><a class="reference internal" href="../../../user_guide/mixed_precision.html">Compile Mixed Precision models with Torch-TensorRT</a></li>
</ul>
<p class="caption" role="heading"><span class="caption-text">Tutorials</span></p>
<ul>
<li class="toctree-l1"><a class="reference internal" href="../../../tutorials/_rendered_examples/dynamo/torch_compile_advanced_usage.html">Torch Compile Advanced Usage</a></li>
<li class="toctree-l1"><a class="reference internal" href="../../../tutorials/_rendered_examples/dynamo/vgg16_ptq.html">Deploy Quantized Models using Torch-TensorRT</a></li>
<li class="toctree-l1"><a class="reference internal" href="../../../tutorials/_rendered_examples/dynamo/engine_caching_example.html">Engine Caching</a></li>
<li class="toctree-l1"><a class="reference internal" href="../../../tutorials/_rendered_examples/dynamo/engine_caching_bert_example.html">Engine Caching (BERT)</a></li>
<li class="toctree-l1"><a class="reference internal" href="../../../tutorials/_rendered_examples/dynamo/refit_engine_example.html">Refitting Torch-TensorRT Programs with New Weights</a></li>
<li class="toctree-l1"><a class="reference internal" href="../../../tutorials/serving_torch_tensorrt_with_triton.html">Serving a Torch-TensorRT model with Triton</a></li>
<li class="toctree-l1"><a class="reference internal" href="../../../tutorials/_rendered_examples/dynamo/torch_export_cudagraphs.html">Torch Export with Cudagraphs</a></li>
<li class="toctree-l1"><a class="reference internal" href="../../../tutorials/_rendered_examples/dynamo/converter_overloading.html">Overloading Torch-TensorRT Converters with Custom Converters</a></li>
<li class="toctree-l1"><a class="reference internal" href="../../../tutorials/_rendered_examples/dynamo/custom_kernel_plugins.html">Using Custom Kernels within TensorRT Engines with Torch-TensorRT</a></li>
<li class="toctree-l1"><a class="reference internal" href="../../../tutorials/_rendered_examples/dynamo/auto_generate_converters.html">Automatically Generate a Converter for a Custom Kernel</a></li>
<li class="toctree-l1"><a class="reference internal" href="../../../tutorials/_rendered_examples/dynamo/auto_generate_plugins.html">Automatically Generate a Plugin for a Custom Kernel</a></li>
<li class="toctree-l1"><a class="reference internal" href="../../../tutorials/_rendered_examples/dynamo/mutable_torchtrt_module_example.html">Mutable Torch TensorRT Module</a></li>
<li class="toctree-l1"><a class="reference internal" href="../../../tutorials/_rendered_examples/dynamo/weight_streaming_example.html">Weight Streaming</a></li>
<li class="toctree-l1"><a class="reference internal" href="../../../tutorials/_rendered_examples/dynamo/pre_allocated_output_example.html">Pre-allocated output buffer</a></li>
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<p class="caption" role="heading"><span class="caption-text">Dynamo Frontend</span></p>
<ul>
<li class="toctree-l1"><a class="reference internal" href="../../../dynamo/torch_compile.html">TensorRT Backend for <code class="docutils literal notranslate"><span class="pre">torch.compile</span></code></a></li>
<li class="toctree-l1"><a class="reference internal" href="../../../dynamo/dynamo_export.html">Compiling Exported Programs with Torch-TensorRT</a></li>
</ul>
<p class="caption" role="heading"><span class="caption-text">TorchScript Frontend</span></p>
<ul>
<li class="toctree-l1"><a class="reference internal" href="../../../ts/creating_torchscript_module_in_python.html">Creating a TorchScript Module</a></li>
<li class="toctree-l1"><a class="reference internal" href="../../../ts/creating_torchscript_module_in_python.html#working-with-torchscript-in-python">Working with TorchScript in Python</a></li>
<li class="toctree-l1"><a class="reference internal" href="../../../ts/creating_torchscript_module_in_python.html#saving-torchscript-module-to-disk">Saving TorchScript Module to Disk</a></li>
<li class="toctree-l1"><a class="reference internal" href="../../../ts/getting_started_with_python_api.html">Using Torch-TensorRT in Python</a></li>
<li class="toctree-l1"><a class="reference internal" href="../../../ts/getting_started_with_cpp_api.html">Using Torch-TensorRT in  C++</a></li>
<li class="toctree-l1"><a class="reference internal" href="../../../ts/ptq.html">Post Training Quantization (PTQ)</a></li>
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<p class="caption" role="heading"><span class="caption-text">FX Frontend</span></p>
<ul>
<li class="toctree-l1"><a class="reference internal" href="../../../fx/getting_started_with_fx_path.html">Torch-TensorRT (FX Frontend) User Guide</a></li>
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<p class="caption" role="heading"><span class="caption-text">Model Zoo</span></p>
<ul>
<li class="toctree-l1"><a class="reference internal" href="../../../tutorials/_rendered_examples/dynamo/torch_compile_resnet_example.html">Compiling ResNet with dynamic shapes using the <cite>torch.compile</cite> backend</a></li>
<li class="toctree-l1"><a class="reference internal" href="../../../tutorials/_rendered_examples/dynamo/torch_compile_transformers_example.html">Compiling BERT using the <cite>torch.compile</cite> backend</a></li>
<li class="toctree-l1"><a class="reference internal" href="../../../tutorials/_rendered_examples/dynamo/torch_compile_stable_diffusion.html">Compiling Stable Diffusion model using the <cite>torch.compile</cite> backend</a></li>
<li class="toctree-l1"><a class="reference internal" href="../../../tutorials/compile_hf_models.html">Compiling LLM models from Huggingface</a></li>
<li class="toctree-l1"><a class="reference internal" href="../../../tutorials/_rendered_examples/dynamo/torch_compile_gpt2.html">Compiling GPT2 using the Torch-TensorRT <code class="docutils literal notranslate"><span class="pre">torch.compile</span></code> frontend</a></li>
<li class="toctree-l1"><a class="reference internal" href="../../../tutorials/_rendered_examples/dynamo/torch_export_sam2.html">Compiling SAM2 using the dynamo backend</a></li>
<li class="toctree-l1"><a class="reference internal" href="../../../tutorials/_rendered_examples/dynamo/torch_export_flux_dev.html">Compiling FLUX.1-dev model using the Torch-TensorRT dynamo backend</a></li>
<li class="toctree-l1"><a class="reference internal" href="../../../tutorials/notebooks.html">Legacy notebooks</a></li>
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<p class="caption" role="heading"><span class="caption-text">Python API Documentation</span></p>
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<li class="toctree-l1"><a class="reference internal" href="../../../py_api/torch_tensorrt.html">torch_tensorrt</a></li>
<li class="toctree-l1"><a class="reference internal" href="../../../py_api/dynamo.html">torch_tensorrt.dynamo</a></li>
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<li class="toctree-l1"><a class="reference internal" href="../../../py_api/fx.html">torch_tensorrt.fx</a></li>
<li class="toctree-l1"><a class="reference internal" href="../../../py_api/ts.html">torch_tensorrt.ts</a></li>
<li class="toctree-l1"><a class="reference internal" href="../../../py_api/ptq.html">torch_tensorrt.ts.ptq</a></li>
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<p class="caption" role="heading"><span class="caption-text">C++ API Documentation</span></p>
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<li class="toctree-l1"><a class="reference internal" href="../../../_cpp_api/torch_tensort_cpp.html">Torch-TensorRT C++ API</a></li>
<li class="toctree-l1"><a class="reference internal" href="../../../_cpp_api/namespace_torch_tensorrt.html">Namespace torch_tensorrt</a></li>
<li class="toctree-l1"><a class="reference internal" href="../../../_cpp_api/namespace_torch_tensorrt__logging.html">Namespace torch_tensorrt::logging</a></li>
<li class="toctree-l1"><a class="reference internal" href="../../../_cpp_api/namespace_torch_tensorrt__torchscript.html">Namespace torch_tensorrt::torchscript</a></li>
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<p class="caption" role="heading"><span class="caption-text">CLI Documentation</span></p>
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<li class="toctree-l1"><a class="reference internal" href="../../../cli/torchtrtc.html">torchtrtc</a></li>
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<p class="caption" role="heading"><span class="caption-text">Contributor Documentation</span></p>
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<li class="toctree-l1"><a class="reference internal" href="../../../contributors/system_overview.html">System Overview</a></li>
<li class="toctree-l1"><a class="reference internal" href="../../../contributors/dynamo_converters.html">Writing Dynamo Converters</a></li>
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  <h1>Source code for torch_tensorrt.dynamo._compiler</h1><div class="highlight"><pre>
<span></span><span class="kn">from</span><span class="w"> </span><span class="nn">__future__</span><span class="w"> </span><span class="kn">import</span> <span class="n">annotations</span>

<span class="kn">import</span><span class="w"> </span><span class="nn">collections.abc</span>
<span class="kn">import</span><span class="w"> </span><span class="nn">logging</span>
<span class="kn">import</span><span class="w"> </span><span class="nn">os</span>
<span class="kn">import</span><span class="w"> </span><span class="nn">platform</span>
<span class="kn">import</span><span class="w"> </span><span class="nn">warnings</span>
<span class="kn">from</span><span class="w"> </span><span class="nn">typing</span><span class="w"> </span><span class="kn">import</span> <span class="n">Any</span><span class="p">,</span> <span class="n">Collection</span><span class="p">,</span> <span class="n">List</span><span class="p">,</span> <span class="n">Optional</span><span class="p">,</span> <span class="n">Sequence</span><span class="p">,</span> <span class="n">Set</span><span class="p">,</span> <span class="n">Tuple</span><span class="p">,</span> <span class="n">Union</span>

<span class="kn">import</span><span class="w"> </span><span class="nn">torch</span>
<span class="kn">from</span><span class="w"> </span><span class="nn">torch.export</span><span class="w"> </span><span class="kn">import</span> <span class="n">ExportedProgram</span>
<span class="kn">from</span><span class="w"> </span><span class="nn">torch.fx.node</span><span class="w"> </span><span class="kn">import</span> <span class="n">Target</span>
<span class="kn">from</span><span class="w"> </span><span class="nn">torch_tensorrt._Device</span><span class="w"> </span><span class="kn">import</span> <span class="n">Device</span>
<span class="kn">from</span><span class="w"> </span><span class="nn">torch_tensorrt._enums</span><span class="w"> </span><span class="kn">import</span> <span class="n">EngineCapability</span><span class="p">,</span> <span class="n">dtype</span>
<span class="kn">from</span><span class="w"> </span><span class="nn">torch_tensorrt._features</span><span class="w"> </span><span class="kn">import</span> <span class="n">needs_cross_compile</span>
<span class="kn">from</span><span class="w"> </span><span class="nn">torch_tensorrt._Input</span><span class="w"> </span><span class="kn">import</span> <span class="n">Input</span>
<span class="kn">from</span><span class="w"> </span><span class="nn">torch_tensorrt.dynamo</span><span class="w"> </span><span class="kn">import</span> <span class="n">_defaults</span><span class="p">,</span> <span class="n">partitioning</span>
<span class="kn">from</span><span class="w"> </span><span class="nn">torch_tensorrt.dynamo._DryRunTracker</span><span class="w"> </span><span class="kn">import</span> <span class="p">(</span>
    <span class="n">DryRunTracker</span><span class="p">,</span>
    <span class="n">PerSubgraphData</span><span class="p">,</span>
    <span class="n">dryrun_stats_display</span><span class="p">,</span>
    <span class="n">parse_non_trt_nodes</span><span class="p">,</span>
<span class="p">)</span>
<span class="kn">from</span><span class="w"> </span><span class="nn">torch_tensorrt.dynamo._engine_cache</span><span class="w"> </span><span class="kn">import</span> <span class="n">BaseEngineCache</span><span class="p">,</span> <span class="n">DiskEngineCache</span>
<span class="kn">from</span><span class="w"> </span><span class="nn">torch_tensorrt.dynamo._exporter</span><span class="w"> </span><span class="kn">import</span> <span class="n">replace_execute_engine_no_op_node</span>
<span class="kn">from</span><span class="w"> </span><span class="nn">torch_tensorrt.dynamo.conversion</span><span class="w"> </span><span class="kn">import</span> <span class="p">(</span>
    <span class="n">CompilationSettings</span><span class="p">,</span>
    <span class="n">UnsupportedOperatorException</span><span class="p">,</span>
    <span class="n">convert_module</span><span class="p">,</span>
    <span class="n">interpret_module_to_result</span><span class="p">,</span>
    <span class="n">repair_double_inputs</span><span class="p">,</span>
<span class="p">)</span>
<span class="kn">from</span><span class="w"> </span><span class="nn">torch_tensorrt.dynamo.conversion._ConverterRegistry</span><span class="w"> </span><span class="kn">import</span> <span class="p">(</span>
    <span class="n">DYNAMO_CONVERTERS</span> <span class="k">as</span> <span class="n">CONVERTERS</span><span class="p">,</span>
<span class="p">)</span>
<span class="kn">from</span><span class="w"> </span><span class="nn">torch_tensorrt.dynamo.debug._DebuggerConfig</span><span class="w"> </span><span class="kn">import</span> <span class="n">DebuggerConfig</span>
<span class="kn">from</span><span class="w"> </span><span class="nn">torch_tensorrt.dynamo.debug._supports_debugger</span><span class="w"> </span><span class="kn">import</span> <span class="n">fn_supports_debugger</span>
<span class="kn">from</span><span class="w"> </span><span class="nn">torch_tensorrt.dynamo.lowering</span><span class="w"> </span><span class="kn">import</span> <span class="p">(</span>
    <span class="n">get_decompositions</span><span class="p">,</span>
    <span class="n">post_lowering</span><span class="p">,</span>
    <span class="n">pre_export_lowering</span><span class="p">,</span>
<span class="p">)</span>
<span class="kn">from</span><span class="w"> </span><span class="nn">torch_tensorrt.dynamo.utils</span><span class="w"> </span><span class="kn">import</span> <span class="p">(</span>
    <span class="n">deallocate_module</span><span class="p">,</span>
    <span class="n">get_cpu_memory_usage</span><span class="p">,</span>
    <span class="n">get_flat_args_with_check</span><span class="p">,</span>
    <span class="n">get_output_metadata</span><span class="p">,</span>
    <span class="n">parse_graph_io</span><span class="p">,</span>
    <span class="n">prepare_inputs</span><span class="p">,</span>
    <span class="n">to_torch_device</span><span class="p">,</span>
    <span class="n">to_torch_tensorrt_device</span><span class="p">,</span>
<span class="p">)</span>

<span class="n">logger</span> <span class="o">=</span> <span class="n">logging</span><span class="o">.</span><span class="n">getLogger</span><span class="p">(</span><span class="vm">__name__</span><span class="p">)</span>


<span class="nd">@needs_cross_compile</span>  <span class="c1"># type: ignore[misc]</span>
<span class="k">def</span><span class="w"> </span><span class="nf">cross_compile_for_windows</span><span class="p">(</span>
    <span class="n">exported_program</span><span class="p">:</span> <span class="n">ExportedProgram</span><span class="p">,</span>
    <span class="n">inputs</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="n">Sequence</span><span class="p">[</span><span class="n">Sequence</span><span class="p">[</span><span class="n">Any</span><span class="p">]]]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
    <span class="o">*</span><span class="p">,</span>
    <span class="n">arg_inputs</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="n">Sequence</span><span class="p">[</span><span class="n">Sequence</span><span class="p">[</span><span class="n">Any</span><span class="p">]]]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
    <span class="n">kwarg_inputs</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="nb">dict</span><span class="p">[</span><span class="n">Any</span><span class="p">,</span> <span class="n">Any</span><span class="p">]]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
    <span class="n">device</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="n">Union</span><span class="p">[</span><span class="n">Device</span><span class="p">,</span> <span class="n">torch</span><span class="o">.</span><span class="n">device</span><span class="p">,</span> <span class="nb">str</span><span class="p">]]</span> <span class="o">=</span> <span class="n">_defaults</span><span class="o">.</span><span class="n">DEVICE</span><span class="p">,</span>
    <span class="n">disable_tf32</span><span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="n">_defaults</span><span class="o">.</span><span class="n">DISABLE_TF32</span><span class="p">,</span>
    <span class="n">assume_dynamic_shape_support</span><span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="n">_defaults</span><span class="o">.</span><span class="n">ASSUME_DYNAMIC_SHAPE_SUPPORT</span><span class="p">,</span>
    <span class="n">sparse_weights</span><span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="n">_defaults</span><span class="o">.</span><span class="n">SPARSE_WEIGHTS</span><span class="p">,</span>
    <span class="n">enabled_precisions</span><span class="p">:</span> <span class="n">Union</span><span class="p">[</span>
        <span class="n">Set</span><span class="p">[</span><span class="n">Union</span><span class="p">[</span><span class="n">torch</span><span class="o">.</span><span class="n">dtype</span><span class="p">,</span> <span class="n">dtype</span><span class="p">]],</span> <span class="n">Tuple</span><span class="p">[</span><span class="n">Union</span><span class="p">[</span><span class="n">torch</span><span class="o">.</span><span class="n">dtype</span><span class="p">,</span> <span class="n">dtype</span><span class="p">]]</span>
    <span class="p">]</span> <span class="o">=</span> <span class="n">_defaults</span><span class="o">.</span><span class="n">ENABLED_PRECISIONS</span><span class="p">,</span>
    <span class="n">engine_capability</span><span class="p">:</span> <span class="n">EngineCapability</span> <span class="o">=</span> <span class="n">_defaults</span><span class="o">.</span><span class="n">ENGINE_CAPABILITY</span><span class="p">,</span>
    <span class="n">num_avg_timing_iters</span><span class="p">:</span> <span class="nb">int</span> <span class="o">=</span> <span class="n">_defaults</span><span class="o">.</span><span class="n">NUM_AVG_TIMING_ITERS</span><span class="p">,</span>
    <span class="n">workspace_size</span><span class="p">:</span> <span class="nb">int</span> <span class="o">=</span> <span class="n">_defaults</span><span class="o">.</span><span class="n">WORKSPACE_SIZE</span><span class="p">,</span>
    <span class="n">dla_sram_size</span><span class="p">:</span> <span class="nb">int</span> <span class="o">=</span> <span class="n">_defaults</span><span class="o">.</span><span class="n">DLA_SRAM_SIZE</span><span class="p">,</span>
    <span class="n">dla_local_dram_size</span><span class="p">:</span> <span class="nb">int</span> <span class="o">=</span> <span class="n">_defaults</span><span class="o">.</span><span class="n">DLA_LOCAL_DRAM_SIZE</span><span class="p">,</span>
    <span class="n">dla_global_dram_size</span><span class="p">:</span> <span class="nb">int</span> <span class="o">=</span> <span class="n">_defaults</span><span class="o">.</span><span class="n">DLA_GLOBAL_DRAM_SIZE</span><span class="p">,</span>
    <span class="n">truncate_double</span><span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="n">_defaults</span><span class="o">.</span><span class="n">TRUNCATE_DOUBLE</span><span class="p">,</span>
    <span class="n">require_full_compilation</span><span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="n">_defaults</span><span class="o">.</span><span class="n">REQUIRE_FULL_COMPILATION</span><span class="p">,</span>
    <span class="n">min_block_size</span><span class="p">:</span> <span class="nb">int</span> <span class="o">=</span> <span class="n">_defaults</span><span class="o">.</span><span class="n">MIN_BLOCK_SIZE</span><span class="p">,</span>
    <span class="n">torch_executed_ops</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="n">Collection</span><span class="p">[</span><span class="n">Target</span><span class="p">]]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
    <span class="n">torch_executed_modules</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="n">List</span><span class="p">[</span><span class="nb">str</span><span class="p">]]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
    <span class="n">pass_through_build_failures</span><span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="n">_defaults</span><span class="o">.</span><span class="n">PASS_THROUGH_BUILD_FAILURES</span><span class="p">,</span>
    <span class="n">max_aux_streams</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="nb">int</span><span class="p">]</span> <span class="o">=</span> <span class="n">_defaults</span><span class="o">.</span><span class="n">MAX_AUX_STREAMS</span><span class="p">,</span>
    <span class="n">version_compatible</span><span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="n">_defaults</span><span class="o">.</span><span class="n">VERSION_COMPATIBLE</span><span class="p">,</span>
    <span class="n">optimization_level</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="nb">int</span><span class="p">]</span> <span class="o">=</span> <span class="n">_defaults</span><span class="o">.</span><span class="n">OPTIMIZATION_LEVEL</span><span class="p">,</span>
    <span class="n">use_python_runtime</span><span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="n">_defaults</span><span class="o">.</span><span class="n">USE_PYTHON_RUNTIME</span><span class="p">,</span>
    <span class="n">use_fast_partitioner</span><span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="n">_defaults</span><span class="o">.</span><span class="n">USE_FAST_PARTITIONER</span><span class="p">,</span>
    <span class="n">enable_experimental_decompositions</span><span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="n">_defaults</span><span class="o">.</span><span class="n">ENABLE_EXPERIMENTAL_DECOMPOSITIONS</span><span class="p">,</span>
    <span class="n">dryrun</span><span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="n">_defaults</span><span class="o">.</span><span class="n">DRYRUN</span><span class="p">,</span>
    <span class="n">hardware_compatible</span><span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="n">_defaults</span><span class="o">.</span><span class="n">HARDWARE_COMPATIBLE</span><span class="p">,</span>
    <span class="n">timing_cache_path</span><span class="p">:</span> <span class="nb">str</span> <span class="o">=</span> <span class="n">_defaults</span><span class="o">.</span><span class="n">TIMING_CACHE_PATH</span><span class="p">,</span>
    <span class="n">lazy_engine_init</span><span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="n">_defaults</span><span class="o">.</span><span class="n">LAZY_ENGINE_INIT</span><span class="p">,</span>
    <span class="n">cache_built_engines</span><span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="n">_defaults</span><span class="o">.</span><span class="n">CACHE_BUILT_ENGINES</span><span class="p">,</span>
    <span class="n">reuse_cached_engines</span><span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="n">_defaults</span><span class="o">.</span><span class="n">REUSE_CACHED_ENGINES</span><span class="p">,</span>
    <span class="n">engine_cache_dir</span><span class="p">:</span> <span class="nb">str</span> <span class="o">=</span> <span class="n">_defaults</span><span class="o">.</span><span class="n">ENGINE_CACHE_DIR</span><span class="p">,</span>
    <span class="n">engine_cache_size</span><span class="p">:</span> <span class="nb">int</span> <span class="o">=</span> <span class="n">_defaults</span><span class="o">.</span><span class="n">ENGINE_CACHE_SIZE</span><span class="p">,</span>
    <span class="n">custom_engine_cache</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="n">BaseEngineCache</span><span class="p">]</span> <span class="o">=</span> <span class="n">_defaults</span><span class="o">.</span><span class="n">CUSTOM_ENGINE_CACHE</span><span class="p">,</span>
    <span class="n">use_explicit_typing</span><span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="n">_defaults</span><span class="o">.</span><span class="n">USE_EXPLICIT_TYPING</span><span class="p">,</span>
    <span class="n">use_fp32_acc</span><span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="n">_defaults</span><span class="o">.</span><span class="n">USE_FP32_ACC</span><span class="p">,</span>
    <span class="n">refit_identical_engine_weights</span><span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="n">_defaults</span><span class="o">.</span><span class="n">REFIT_IDENTICAL_ENGINE_WEIGHTS</span><span class="p">,</span>
    <span class="n">strip_engine_weights</span><span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="n">_defaults</span><span class="o">.</span><span class="n">STRIP_ENGINE_WEIGHTS</span><span class="p">,</span>
    <span class="n">immutable_weights</span><span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="n">_defaults</span><span class="o">.</span><span class="n">IMMUTABLE_WEIGHTS</span><span class="p">,</span>
    <span class="n">enable_weight_streaming</span><span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="n">_defaults</span><span class="o">.</span><span class="n">ENABLE_WEIGHT_STREAMING</span><span class="p">,</span>
    <span class="n">tiling_optimization_level</span><span class="p">:</span> <span class="nb">str</span> <span class="o">=</span> <span class="n">_defaults</span><span class="o">.</span><span class="n">TILING_OPTIMIZATION_LEVEL</span><span class="p">,</span>
    <span class="n">l2_limit_for_tiling</span><span class="p">:</span> <span class="nb">int</span> <span class="o">=</span> <span class="n">_defaults</span><span class="o">.</span><span class="n">L2_LIMIT_FOR_TILING</span><span class="p">,</span>
    <span class="n">offload_module_to_cpu</span><span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="n">_defaults</span><span class="o">.</span><span class="n">OFFLOAD_MODULE_TO_CPU</span><span class="p">,</span>
    <span class="n">use_distributed_mode_trace</span><span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="n">_defaults</span><span class="o">.</span><span class="n">USE_DISTRIBUTED_MODE_TRACE</span><span class="p">,</span>
    <span class="o">**</span><span class="n">kwargs</span><span class="p">:</span> <span class="n">Any</span><span class="p">,</span>
<span class="p">)</span> <span class="o">-&gt;</span> <span class="n">torch</span><span class="o">.</span><span class="n">fx</span><span class="o">.</span><span class="n">GraphModule</span><span class="p">:</span>
<span class="w">    </span><span class="sd">&quot;&quot;&quot;Compile an ExportedProgram module using TensorRT in Linux for Inference in Windows</span>

<span class="sd">    Takes an exported program and a set of settings to configure the compiler</span>
<span class="sd">    and it will convert methods to AOT graphs which call equivalent TensorRT engines</span>

<span class="sd">    Arguments:</span>
<span class="sd">        exported_program (torch.export.ExportedProgram): Source module, running torch.export on a ``torch.nn.Module``</span>
<span class="sd">        inputs (Tuple[Any, ...]): List of specifications of input shape, dtype and memory layout for inputs to the module. This argument is required. Input Sizes can be specified as torch sizes, tuples or lists. dtypes can be specified using</span>
<span class="sd">            torch datatypes or torch_tensorrt datatypes and you can use either torch devices or the torch_tensorrt device type enum</span>
<span class="sd">            to select device type.</span>

<span class="sd">                .. code-block:: py</span>

<span class="sd">                    inputs=[</span>
<span class="sd">                        torch_tensorrt.Input((1, 3, 224, 224)), # Static NCHW input shape for input #1</span>
<span class="sd">                        torch_tensorrt.Input(</span>
<span class="sd">                            min_shape=(1, 224, 224, 3),</span>
<span class="sd">                            opt_shape=(1, 512, 512, 3),</span>
<span class="sd">                            max_shape=(1, 1024, 1024, 3),</span>
<span class="sd">                            dtype=torch.int32</span>
<span class="sd">                            format=torch.channel_last</span>
<span class="sd">                        ), # Dynamic input shape for input #2</span>
<span class="sd">                        torch.randn((1, 3, 224, 244)) # Use an example tensor and let torch_tensorrt infer settings</span>
<span class="sd">                    ]</span>

<span class="sd">    Keyword Arguments:</span>
<span class="sd">        arg_inputs (Tuple[Any, ...]): Same as inputs. Alias for better understanding with kwarg_inputs.</span>
<span class="sd">        kwarg_inputs (dict[Any, ...]): Optional, kwarg inputs to the module forward function.</span>
<span class="sd">        device (Union(torch_tensorrt.Device, torch.device, dict)): Target device for TensorRT engines to run on ::</span>

<span class="sd">            device=torch_tensorrt.Device(&quot;dla:1&quot;, allow_gpu_fallback=True)</span>

<span class="sd">        disable_tf32 (bool): Force FP32 layers to use traditional as FP32 format vs the default behavior of rounding the inputs to 10-bit mantissas before multiplying, but accumulates the sum using 23-bit mantissas</span>
<span class="sd">        assume_dynamic_shape_support (bool): Setting this to true enables the converters work for both dynamic and static shapes. Default: False</span>
<span class="sd">        sparse_weights (bool): Enable sparsity for convolution and fully connected layers.</span>
<span class="sd">        enabled_precision (Set(Union(torch.dtype, torch_tensorrt.dtype))): The set of datatypes that TensorRT can use when selecting kernels</span>
<span class="sd">        capability (torch_tensorrt.EngineCapability): Restrict kernel selection to safe gpu kernels or safe dla kernels</span>
<span class="sd">        num_avg_timing_iters (int): Number of averaging timing iterations used to select kernels</span>
<span class="sd">        workspace_size (int): Maximum size of workspace given to TensorRT</span>
<span class="sd">        dla_sram_size (int): Fast software managed RAM used by DLA to communicate within a layer.</span>
<span class="sd">        dla_local_dram_size (int): Host RAM used by DLA to share intermediate tensor data across operations</span>
<span class="sd">        dla_global_dram_size (int): Host RAM used by DLA to store weights and metadata for execution</span>
<span class="sd">        truncate_double (bool): Truncate weights provided in double (float64) to float32</span>
<span class="sd">        require_full_compilation (bool): Require modules to be compiled end to end or return an error as opposed to returning a hybrid graph where operations that cannot be run in TensorRT are run in PyTorch</span>
<span class="sd">        min_block_size (int): The minimum number of contiguous TensorRT convertible operations in order to run a set of operations in TensorRT</span>
<span class="sd">        torch_executed_ops (Collection[Target]): Set of aten operators that must be run in PyTorch. An error will be thrown if this set is not empty but ``require_full_compilation`` is True</span>
<span class="sd">        torch_executed_modules (List[str]): List of modules that must be run in PyTorch. An error will be thrown if this list is not empty but ``require_full_compilation`` is True</span>
<span class="sd">        pass_through_build_failures (bool): Error out if there are issues during compilation (only applicable to torch.compile workflows)</span>
<span class="sd">        max_aux_stream (Optional[int]): Maximum streams in the engine</span>
<span class="sd">        version_compatible (bool): Build the TensorRT engines compatible with future versions of TensorRT (Restrict to lean runtime operators to provide version forward compatibility for the engines)</span>
<span class="sd">        optimization_level: (Optional[int]): Setting a higher optimization level allows TensorRT to spend longer engine building time searching for more optimization options. The resulting engine may have better performance compared to an engine built with a lower optimization level. The default optimization level is 3. Valid values include integers from 0 to the maximum optimization level, which is currently 5. Setting it to be greater than the maximum level results in identical behavior to the maximum level.</span>
<span class="sd">        use_python_runtime: (bool): Return a graph using a pure Python runtime, reduces options for serialization</span>
<span class="sd">        use_fast_partitioner: (bool): Use the adjacency based partitioning scheme instead of the global partitioner. Adjacency partitioning is faster but may not be optimal. Use the global paritioner (``False``) if looking for best performance</span>
<span class="sd">        enable_experimental_decompositions (bool): Use the full set of operator decompositions. These decompositions may not be tested but serve to make the graph easier to convert to TensorRT, potentially increasing the amount of graphs run in TensorRT.</span>
<span class="sd">        dryrun (bool): Toggle for &quot;Dryrun&quot; mode, running everything except conversion to TRT and logging outputs</span>
<span class="sd">        hardware_compatible (bool): Build the TensorRT engines compatible with GPU architectures other than that of the GPU on which the engine was built (currently works for NVIDIA Ampere and newer)</span>
<span class="sd">        timing_cache_path (str): Path to the timing cache if it exists (or) where it will be saved after compilation</span>
<span class="sd">        lazy_engine_init (bool): Defer setting up engines until the compilation of all engines is complete. Can allow larger models with multiple graph breaks to compile but can lead to oversubscription of GPU memory at runtime.</span>
<span class="sd">        cache_built_engines (bool): Whether to save the compiled TRT engines to storage</span>
<span class="sd">        reuse_cached_engines (bool): Whether to load the compiled TRT engines from storage</span>
<span class="sd">        engine_cache_dir (Optional[str]): Directory to store the cached TRT engines</span>
<span class="sd">        engine_cache_size (Optional[int]): Maximum hard-disk space (bytes) to use for the engine cache, default is 1GB. If the cache exceeds this size, the oldest engines will be removed by default</span>
<span class="sd">        custom_engine_cache (Optional[BaseEngineCache]): Engine cache instance to use for saving and loading engines. Users can provide their own engine cache by inheriting from BaseEngineCache. If used, engine_cache_dir and engine_cache_size will be ignored.</span>
<span class="sd">        use_explicit_typing (bool): This flag enables strong typing in TensorRT compilation which respects the precisions set in the Pytorch model. This is useful when users have mixed precision graphs.</span>
<span class="sd">        use_fp32_acc (bool): This option inserts cast to FP32 nodes around matmul layers and TensorRT ensures the accumulation of matmul happens in FP32. Use this only when FP16 precision is configured in enabled_precisions.</span>
<span class="sd">        refit_identical_engine_weights (bool): Refit engines with identical weights. This is useful when the same model is compiled multiple times with different inputs and the weights are the same. This will save time by reusing the same engine for different inputs.</span>
<span class="sd">        strip_engine_weights (bool): Strip engine weights from the serialized engine. This is useful when the engine is to be deployed in an environment where the weights are not required.</span>
<span class="sd">        immutable_weights (bool): Build non-refittable engines. This is useful for some layers that are not refittable. If this argument is set to true, `strip_engine_weights` and `refit_identical_engine_weights` will be ignored.</span>
<span class="sd">        enable_weight_streaming (bool): Enable weight streaming.</span>
<span class="sd">        tiling_optimization_level (str): The optimization level of tiling strategies. A higher level allows TensorRT to spend more time searching for better tiling strategy. We currently support [&quot;none&quot;, &quot;fast&quot;, &quot;moderate&quot;, &quot;full&quot;].</span>
<span class="sd">        l2_limit_for_tiling (int): The target L2 cache usage limit (in bytes) for tiling optimization (default is -1 which means no limit).</span>
<span class="sd">        use_distributed_mode_trace (bool):  Using aot_autograd to trace the graph. This is enabled when DTensors or distributed tensors are present in distributed model</span>
<span class="sd">        **kwargs: Any,</span>
<span class="sd">    Returns:</span>
<span class="sd">        torch.fx.GraphModule: Compiled FX Module, when run it will execute via TensorRT</span>

<span class="sd">    &quot;&quot;&quot;</span>
    <span class="k">if</span> <span class="n">platform</span><span class="o">.</span><span class="n">system</span><span class="p">()</span> <span class="o">!=</span> <span class="s2">&quot;Linux&quot;</span> <span class="ow">or</span> <span class="n">platform</span><span class="o">.</span><span class="n">architecture</span><span class="p">()[</span><span class="mi">0</span><span class="p">]</span> <span class="o">!=</span> <span class="s2">&quot;64bit&quot;</span><span class="p">:</span>
        <span class="k">raise</span> <span class="ne">RuntimeError</span><span class="p">(</span>
            <span class="sa">f</span><span class="s2">&quot;Cross compile for windows is only supported on x86-64 Linux architecture, current platform: </span><span class="si">{</span><span class="n">platform</span><span class="o">.</span><span class="n">system</span><span class="p">()</span><span class="si">=}</span><span class="s2">, </span><span class="si">{</span><span class="n">platform</span><span class="o">.</span><span class="n">architecture</span><span class="p">()[</span><span class="mi">0</span><span class="p">]</span><span class="si">=}</span><span class="s2">&quot;</span>
        <span class="p">)</span>

    <span class="k">if</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s2">&quot;debug&quot;</span><span class="p">,</span> <span class="kc">False</span><span class="p">):</span>
        <span class="n">warnings</span><span class="o">.</span><span class="n">warn</span><span class="p">(</span>
            <span class="s2">&quot;`debug` is deprecated. Please use `with torch_tensorrt.dynamo.Debugger(...)` to wrap your compilation call to enable debugging functionality.&quot;</span><span class="p">,</span>
            <span class="ne">DeprecationWarning</span><span class="p">,</span>
            <span class="n">stacklevel</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span>
        <span class="p">)</span>

    <span class="k">if</span> <span class="s2">&quot;truncate_long_and_double&quot;</span> <span class="ow">in</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">keys</span><span class="p">():</span>
        <span class="k">if</span> <span class="n">truncate_double</span> <span class="ow">is</span> <span class="ow">not</span> <span class="n">_defaults</span><span class="o">.</span><span class="n">TRUNCATE_DOUBLE</span><span class="p">:</span>
            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span>
                <span class="s1">&#39;Provided configuration for &quot;truncate_double&quot; and deprecated API &quot;truncate_long_and_double&quot;, please only use &quot;truncate_double&quot;&#39;</span>
            <span class="p">)</span>
        <span class="k">else</span><span class="p">:</span>
            <span class="n">truncate_double</span> <span class="o">=</span> <span class="n">kwargs</span><span class="p">[</span><span class="s2">&quot;truncate_long_and_double&quot;</span><span class="p">]</span>
            <span class="n">warnings</span><span class="o">.</span><span class="n">warn</span><span class="p">(</span>
                <span class="s1">&#39;Compiler option &quot;truncate_long_and_double&quot; is deprecated in favor of &quot;truncate_double&quot; as int64 is now natively supported, this option will be removed in the next version&#39;</span><span class="p">,</span>
                <span class="ne">DeprecationWarning</span><span class="p">,</span>
                <span class="n">stacklevel</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span>
            <span class="p">)</span>

    <span class="k">if</span> <span class="s2">&quot;refit&quot;</span> <span class="ow">in</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">keys</span><span class="p">():</span>
        <span class="n">warnings</span><span class="o">.</span><span class="n">warn</span><span class="p">(</span>
            <span class="s2">&quot;`refit` is deprecated. Please set `immutable_weights=False` to build a refittable engine whose weights can be refitted.&quot;</span><span class="p">,</span>
            <span class="ne">DeprecationWarning</span><span class="p">,</span>
            <span class="n">stacklevel</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span>
        <span class="p">)</span>
        <span class="k">if</span> <span class="n">immutable_weights</span><span class="p">:</span>
            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span>
                <span class="s2">&quot;Use flag `immutable_weights` only. Flag `refit` is deprecated.&quot;</span>
            <span class="p">)</span>
        <span class="k">else</span><span class="p">:</span>
            <span class="n">immutable_weights</span> <span class="o">=</span> <span class="ow">not</span> <span class="n">kwargs</span><span class="p">[</span><span class="s2">&quot;refit&quot;</span><span class="p">]</span>

    <span class="k">if</span> <span class="s2">&quot;make_refittable&quot;</span> <span class="ow">in</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">keys</span><span class="p">():</span>
        <span class="n">warnings</span><span class="o">.</span><span class="n">warn</span><span class="p">(</span>
            <span class="s2">&quot;`make_refittable` is deprecated. Please set `immutable_weights=False` to build a refittable engine whose weights can be refitted&quot;</span><span class="p">,</span>
            <span class="ne">DeprecationWarning</span><span class="p">,</span>
            <span class="n">stacklevel</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span>
        <span class="p">)</span>
        <span class="k">if</span> <span class="n">immutable_weights</span><span class="p">:</span>
            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span>
                <span class="s2">&quot;Use flag `immutable_weights` only. Flag `make_refittable` is deprecated.&quot;</span>
            <span class="p">)</span>
        <span class="k">else</span><span class="p">:</span>
            <span class="n">immutable_weights</span> <span class="o">=</span> <span class="ow">not</span> <span class="n">kwargs</span><span class="p">[</span><span class="s2">&quot;make_refittable&quot;</span><span class="p">]</span>

    <span class="k">if</span> <span class="n">refit_identical_engine_weights</span><span class="p">:</span>
        <span class="k">if</span> <span class="n">immutable_weights</span><span class="p">:</span>
            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span>
                <span class="s2">&quot;`immutable_weights` must be False when `refit_identical_engine_weights` is True.&quot;</span>
            <span class="p">)</span>

    <span class="n">engine_capability</span> <span class="o">=</span> <span class="n">EngineCapability</span><span class="o">.</span><span class="n">_from</span><span class="p">(</span><span class="n">engine_capability</span><span class="p">)</span>

    <span class="k">if</span> <span class="n">torch_executed_modules</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span> <span class="ow">and</span> <span class="n">torch_executed_modules</span><span class="p">:</span>
        <span class="n">logger</span><span class="o">.</span><span class="n">warning</span><span class="p">(</span>
            <span class="sa">f</span><span class="s2">&quot;Detected torch_executed_modules was non-empty: </span><span class="si">{</span><span class="n">torch_executed_modules</span><span class="si">}</span><span class="s2">&quot;</span>
            <span class="s2">&quot;</span><span class="se">\n</span><span class="s2">This feature is unimplemented in Torch-TRT Dynamo currently.&quot;</span>
        <span class="p">)</span>

    <span class="k">if</span> <span class="n">use_explicit_typing</span><span class="p">:</span>
        <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">enabled_precisions</span><span class="p">)</span> <span class="o">!=</span> <span class="mi">1</span> <span class="ow">or</span> <span class="ow">not</span> <span class="nb">any</span><span class="p">(</span>
            <span class="n">x</span> <span class="ow">in</span> <span class="n">enabled_precisions</span>
            <span class="k">for</span> <span class="n">x</span> <span class="ow">in</span> <span class="p">{</span><span class="n">torch</span><span class="o">.</span><span class="n">float32</span><span class="p">,</span> <span class="n">dtype</span><span class="o">.</span><span class="n">f32</span><span class="p">,</span> <span class="n">torch</span><span class="o">.</span><span class="n">float4_e2m1fn_x2</span><span class="p">,</span> <span class="n">dtype</span><span class="o">.</span><span class="n">f4</span><span class="p">}</span>
        <span class="p">):</span>
            <span class="k">raise</span> <span class="ne">AssertionError</span><span class="p">(</span>
                <span class="sa">f</span><span class="s2">&quot;use_explicit_typing was set to True, however found that enabled_precisions was also specified (saw: </span><span class="si">{</span><span class="n">enabled_precisions</span><span class="si">}</span><span class="s2">, expected: dtype.f32, dtype.f4). enabled_precisions should not be used when use_explicit_typing=True&quot;</span>
            <span class="p">)</span>

    <span class="k">if</span> <span class="n">use_fp32_acc</span><span class="p">:</span>
        <span class="n">logger</span><span class="o">.</span><span class="n">debug</span><span class="p">(</span>
            <span class="s2">&quot;FP32 accumulation for matmul layers is enabled. This option should only be enabled if the model already has FP16 weights and has no effect if it has FP32 weights. </span><span class="se">\</span>
<span class="s2">                     This flag inserts casts around matmul layers and ensures TensorRT executes the matmul layers in FP16 with FP32 accumulation.&quot;</span>
        <span class="p">)</span>

    <span class="k">if</span> <span class="n">enable_weight_streaming</span> <span class="ow">and</span> <span class="ow">not</span> <span class="n">use_explicit_typing</span><span class="p">:</span>
        <span class="k">raise</span> <span class="ne">AssertionError</span><span class="p">(</span>
            <span class="s2">&quot;When enable_weight_streaming is enabled, it requires use_explicit_typing to be set to True&quot;</span>
        <span class="p">)</span>
    <span class="c1"># Aliasing inputs to arg_inputs for better understanding</span>
    <span class="k">if</span> <span class="n">arg_inputs</span> <span class="ow">is</span> <span class="kc">None</span> <span class="ow">and</span> <span class="n">kwarg_inputs</span> <span class="ow">is</span> <span class="kc">None</span> <span class="ow">and</span> <span class="n">inputs</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
        <span class="k">raise</span> <span class="ne">AssertionError</span><span class="p">(</span>
            <span class="s2">&quot;&#39;arg_inputs&#39;, &#39;kwarg_inputs&#39; and &#39;inputs&#39; should not all be None.&quot;</span>
        <span class="p">)</span>

    <span class="k">elif</span> <span class="n">arg_inputs</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span> <span class="ow">and</span> <span class="n">inputs</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
        <span class="k">raise</span> <span class="ne">AssertionError</span><span class="p">(</span>
            <span class="s2">&quot;&#39;arg_inputs&#39; and &#39;inputs&#39; should not be used at the same time.&quot;</span>
        <span class="p">)</span>

    <span class="n">arg_inputs</span> <span class="o">=</span> <span class="n">inputs</span> <span class="ow">or</span> <span class="n">arg_inputs</span>

    <span class="k">if</span> <span class="n">kwarg_inputs</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
        <span class="n">kwarg_inputs</span> <span class="o">=</span> <span class="p">{}</span>

    <span class="k">if</span> <span class="ow">not</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">arg_inputs</span><span class="p">,</span> <span class="n">collections</span><span class="o">.</span><span class="n">abc</span><span class="o">.</span><span class="n">Sequence</span><span class="p">):</span>
        <span class="n">arg_inputs</span> <span class="o">=</span> <span class="p">[</span><span class="n">arg_inputs</span><span class="p">]</span>  <span class="c1"># type: ignore</span>

    <span class="c1"># Prepare torch_trt inputs</span>
    <span class="n">trt_arg_inputs</span><span class="p">:</span> <span class="n">Sequence</span><span class="p">[</span><span class="n">Input</span><span class="p">]</span> <span class="o">=</span> <span class="n">prepare_inputs</span><span class="p">(</span><span class="n">arg_inputs</span><span class="p">)</span>
    <span class="n">trt_kwarg_inputs</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="nb">dict</span><span class="p">[</span><span class="n">Any</span><span class="p">,</span> <span class="n">Any</span><span class="p">]]</span> <span class="o">=</span> <span class="n">prepare_inputs</span><span class="p">(</span><span class="n">kwarg_inputs</span><span class="p">)</span>
    <span class="n">device</span> <span class="o">=</span> <span class="n">to_torch_tensorrt_device</span><span class="p">(</span><span class="n">device</span><span class="p">)</span>
    <span class="n">enabled_precisions</span> <span class="o">=</span> <span class="p">{</span><span class="n">dtype</span><span class="o">.</span><span class="n">_from</span><span class="p">(</span><span class="n">p</span><span class="p">)</span> <span class="k">for</span> <span class="n">p</span> <span class="ow">in</span> <span class="n">enabled_precisions</span><span class="p">}</span>

    <span class="n">compilation_options</span> <span class="o">=</span> <span class="p">{</span>
        <span class="s2">&quot;enabled_precisions&quot;</span><span class="p">:</span> <span class="p">(</span>
            <span class="n">enabled_precisions</span> <span class="k">if</span> <span class="n">enabled_precisions</span> <span class="k">else</span> <span class="n">_defaults</span><span class="o">.</span><span class="n">ENABLED_PRECISIONS</span>
        <span class="p">),</span>
        <span class="s2">&quot;device&quot;</span><span class="p">:</span> <span class="n">device</span><span class="p">,</span>
        <span class="s2">&quot;assume_dynamic_shape_support&quot;</span><span class="p">:</span> <span class="n">assume_dynamic_shape_support</span><span class="p">,</span>
        <span class="s2">&quot;workspace_size&quot;</span><span class="p">:</span> <span class="n">workspace_size</span><span class="p">,</span>
        <span class="s2">&quot;min_block_size&quot;</span><span class="p">:</span> <span class="n">min_block_size</span><span class="p">,</span>
        <span class="s2">&quot;torch_executed_ops&quot;</span><span class="p">:</span> <span class="p">(</span>
            <span class="n">torch_executed_ops</span> <span class="k">if</span> <span class="n">torch_executed_ops</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span> <span class="k">else</span> <span class="nb">set</span><span class="p">()</span>
        <span class="p">),</span>
        <span class="s2">&quot;pass_through_build_failures&quot;</span><span class="p">:</span> <span class="n">pass_through_build_failures</span><span class="p">,</span>
        <span class="s2">&quot;max_aux_streams&quot;</span><span class="p">:</span> <span class="n">max_aux_streams</span><span class="p">,</span>
        <span class="s2">&quot;version_compatible&quot;</span><span class="p">:</span> <span class="n">version_compatible</span><span class="p">,</span>
        <span class="s2">&quot;optimization_level&quot;</span><span class="p">:</span> <span class="n">optimization_level</span><span class="p">,</span>
        <span class="s2">&quot;use_python_runtime&quot;</span><span class="p">:</span> <span class="kc">False</span><span class="p">,</span>
        <span class="s2">&quot;truncate_double&quot;</span><span class="p">:</span> <span class="n">truncate_double</span><span class="p">,</span>
        <span class="s2">&quot;use_fast_partitioner&quot;</span><span class="p">:</span> <span class="n">use_fast_partitioner</span><span class="p">,</span>
        <span class="s2">&quot;num_avg_timing_iters&quot;</span><span class="p">:</span> <span class="n">num_avg_timing_iters</span><span class="p">,</span>
        <span class="s2">&quot;enable_experimental_decompositions&quot;</span><span class="p">:</span> <span class="n">enable_experimental_decompositions</span><span class="p">,</span>
        <span class="s2">&quot;require_full_compilation&quot;</span><span class="p">:</span> <span class="n">require_full_compilation</span><span class="p">,</span>
        <span class="s2">&quot;disable_tf32&quot;</span><span class="p">:</span> <span class="n">disable_tf32</span><span class="p">,</span>
        <span class="s2">&quot;sparse_weights&quot;</span><span class="p">:</span> <span class="n">sparse_weights</span><span class="p">,</span>
        <span class="s2">&quot;engine_capability&quot;</span><span class="p">:</span> <span class="n">engine_capability</span><span class="p">,</span>
        <span class="s2">&quot;dla_sram_size&quot;</span><span class="p">:</span> <span class="n">dla_sram_size</span><span class="p">,</span>
        <span class="s2">&quot;dla_local_dram_size&quot;</span><span class="p">:</span> <span class="n">dla_local_dram_size</span><span class="p">,</span>
        <span class="s2">&quot;dla_global_dram_size&quot;</span><span class="p">:</span> <span class="n">dla_global_dram_size</span><span class="p">,</span>
        <span class="s2">&quot;dryrun&quot;</span><span class="p">:</span> <span class="n">dryrun</span><span class="p">,</span>
        <span class="s2">&quot;hardware_compatible&quot;</span><span class="p">:</span> <span class="n">hardware_compatible</span><span class="p">,</span>
        <span class="s2">&quot;timing_cache_path&quot;</span><span class="p">:</span> <span class="n">timing_cache_path</span><span class="p">,</span>
        <span class="s2">&quot;lazy_engine_init&quot;</span><span class="p">:</span> <span class="n">lazy_engine_init</span><span class="p">,</span>
        <span class="s2">&quot;cache_built_engines&quot;</span><span class="p">:</span> <span class="n">cache_built_engines</span><span class="p">,</span>
        <span class="s2">&quot;reuse_cached_engines&quot;</span><span class="p">:</span> <span class="n">reuse_cached_engines</span><span class="p">,</span>
        <span class="s2">&quot;refit_identical_engine_weights&quot;</span><span class="p">:</span> <span class="n">refit_identical_engine_weights</span><span class="p">,</span>
        <span class="s2">&quot;strip_engine_weights&quot;</span><span class="p">:</span> <span class="n">strip_engine_weights</span><span class="p">,</span>
        <span class="s2">&quot;immutable_weights&quot;</span><span class="p">:</span> <span class="n">immutable_weights</span><span class="p">,</span>
        <span class="s2">&quot;enable_cross_compile_for_windows&quot;</span><span class="p">:</span> <span class="kc">True</span><span class="p">,</span>
        <span class="s2">&quot;enable_weight_streaming&quot;</span><span class="p">:</span> <span class="n">enable_weight_streaming</span><span class="p">,</span>
        <span class="s2">&quot;tiling_optimization_level&quot;</span><span class="p">:</span> <span class="n">tiling_optimization_level</span><span class="p">,</span>
        <span class="s2">&quot;l2_limit_for_tiling&quot;</span><span class="p">:</span> <span class="n">l2_limit_for_tiling</span><span class="p">,</span>
        <span class="s2">&quot;use_distributed_mode_trace&quot;</span><span class="p">:</span> <span class="n">use_distributed_mode_trace</span><span class="p">,</span>
    <span class="p">}</span>

    <span class="c1"># disable the following settings is not supported for cross compilation for windows feature</span>
    <span class="n">unsupported_settings</span> <span class="o">=</span> <span class="p">(</span>
        <span class="s2">&quot;use_python_runtime&quot;</span><span class="p">,</span>
        <span class="s2">&quot;lazy_engine_init&quot;</span><span class="p">,</span>
        <span class="s2">&quot;cache_built_engines&quot;</span><span class="p">,</span>
        <span class="s2">&quot;reuse_cached_engines&quot;</span><span class="p">,</span>
    <span class="p">)</span>
    <span class="c1"># disable these settings if anything is turned on</span>
    <span class="k">for</span> <span class="n">key</span><span class="p">,</span> <span class="n">value</span> <span class="ow">in</span> <span class="n">compilation_options</span><span class="o">.</span><span class="n">items</span><span class="p">():</span>
        <span class="k">if</span> <span class="n">key</span> <span class="ow">in</span> <span class="n">unsupported_settings</span> <span class="ow">and</span> <span class="n">value</span><span class="p">:</span>
            <span class="n">compilation_options</span><span class="p">[</span><span class="n">key</span><span class="p">]</span> <span class="o">=</span> <span class="kc">False</span>
            <span class="n">logger</span><span class="o">.</span><span class="n">warning</span><span class="p">(</span>
                <span class="sa">f</span><span class="s2">&quot;arg: </span><span class="si">{</span><span class="n">key</span><span class="si">}</span><span class="s2"> is not supported for cross compilation for windows feature, hence it is disabled.&quot;</span>
            <span class="p">)</span>

    <span class="n">settings</span> <span class="o">=</span> <span class="n">CompilationSettings</span><span class="p">(</span><span class="o">**</span><span class="n">compilation_options</span><span class="p">)</span>
    <span class="n">logger</span><span class="o">.</span><span class="n">info</span><span class="p">(</span><span class="s2">&quot;Compilation Settings: </span><span class="si">%s</span><span class="se">\n</span><span class="s2">&quot;</span><span class="p">,</span> <span class="n">settings</span><span class="p">)</span>
    <span class="n">exported_program</span> <span class="o">=</span> <span class="n">pre_export_lowering</span><span class="p">(</span><span class="n">exported_program</span><span class="p">,</span> <span class="n">settings</span><span class="p">)</span>
    <span class="n">exported_program</span> <span class="o">=</span> <span class="n">exported_program</span><span class="o">.</span><span class="n">run_decompositions</span><span class="p">(</span>
        <span class="n">get_decompositions</span><span class="p">(</span><span class="n">enable_experimental_decompositions</span><span class="p">)</span>
    <span class="p">)</span>

    <span class="n">gm</span> <span class="o">=</span> <span class="n">exported_program</span><span class="o">.</span><span class="n">module</span><span class="p">()</span>
    <span class="n">logger</span><span class="o">.</span><span class="n">debug</span><span class="p">(</span><span class="s2">&quot;Input graph: &quot;</span> <span class="o">+</span> <span class="nb">str</span><span class="p">(</span><span class="n">gm</span><span class="o">.</span><span class="n">graph</span><span class="p">))</span>

    <span class="c1"># Apply lowering on the graph module</span>
    <span class="n">gm</span> <span class="o">=</span> <span class="n">post_lowering</span><span class="p">(</span><span class="n">gm</span><span class="p">,</span> <span class="n">settings</span><span class="p">)</span>
    <span class="n">logger</span><span class="o">.</span><span class="n">debug</span><span class="p">(</span><span class="s2">&quot;Lowered Input graph: &quot;</span> <span class="o">+</span> <span class="nb">str</span><span class="p">(</span><span class="n">gm</span><span class="o">.</span><span class="n">graph</span><span class="p">))</span>
    <span class="c1"># Move the weights in the state_dict to CPU</span>
    <span class="k">if</span> <span class="n">offload_module_to_cpu</span><span class="p">:</span>
        <span class="n">deallocate_module</span><span class="p">(</span><span class="n">exported_program</span><span class="o">.</span><span class="n">module</span><span class="p">(),</span> <span class="n">delete_module</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
        <span class="n">logger</span><span class="o">.</span><span class="n">info</span><span class="p">(</span>
            <span class="s2">&quot;The PyTorch model was moved to the CPU to allocate all GPU memory to TensorRT. To retain the model on the GPU, set offload_module_to_cpu=False&quot;</span>
        <span class="p">)</span>
    <span class="k">else</span><span class="p">:</span>
        <span class="n">remaining_memory</span><span class="p">,</span> <span class="n">total_memory</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">cuda</span><span class="o">.</span><span class="n">mem_get_info</span><span class="p">()</span>
        <span class="k">if</span> <span class="n">remaining_memory</span> <span class="o">&lt;</span> <span class="n">total_memory</span> <span class="o">//</span> <span class="mi">2</span><span class="p">:</span>
            <span class="n">logger</span><span class="o">.</span><span class="n">warning</span><span class="p">(</span>
                <span class="s2">&quot;Remaining GPU memory may not be enough to compile the TensorRT engine for this model resulting in an OOM error, Consider setting offload_module_to_cpu=True&quot;</span>
            <span class="p">)</span>
    <span class="n">trt_gm</span> <span class="o">=</span> <span class="n">compile_module</span><span class="p">(</span>
        <span class="n">gm</span><span class="p">,</span>
        <span class="n">trt_arg_inputs</span><span class="p">,</span>
        <span class="n">trt_kwarg_inputs</span><span class="p">,</span>
        <span class="n">settings</span><span class="p">,</span>
    <span class="p">)</span>
    <span class="k">return</span> <span class="n">trt_gm</span>


<div class="viewcode-block" id="compile"><a class="viewcode-back" href="../../../py_api/dynamo.html#torch_tensorrt.dynamo.compile">[docs]</a><span class="k">def</span><span class="w"> </span><span class="nf">compile</span><span class="p">(</span>
    <span class="n">exported_program</span><span class="p">:</span> <span class="n">ExportedProgram</span><span class="p">,</span>
    <span class="n">inputs</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="n">Sequence</span><span class="p">[</span><span class="n">Sequence</span><span class="p">[</span><span class="n">Any</span><span class="p">]]]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
    <span class="o">*</span><span class="p">,</span>
    <span class="n">arg_inputs</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="n">Sequence</span><span class="p">[</span><span class="n">Sequence</span><span class="p">[</span><span class="n">Any</span><span class="p">]]]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
    <span class="n">kwarg_inputs</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="nb">dict</span><span class="p">[</span><span class="n">Any</span><span class="p">,</span> <span class="n">Any</span><span class="p">]]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
    <span class="n">device</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="n">Union</span><span class="p">[</span><span class="n">Device</span><span class="p">,</span> <span class="n">torch</span><span class="o">.</span><span class="n">device</span><span class="p">,</span> <span class="nb">str</span><span class="p">]]</span> <span class="o">=</span> <span class="n">_defaults</span><span class="o">.</span><span class="n">DEVICE</span><span class="p">,</span>
    <span class="n">disable_tf32</span><span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="n">_defaults</span><span class="o">.</span><span class="n">DISABLE_TF32</span><span class="p">,</span>
    <span class="n">assume_dynamic_shape_support</span><span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="n">_defaults</span><span class="o">.</span><span class="n">ASSUME_DYNAMIC_SHAPE_SUPPORT</span><span class="p">,</span>
    <span class="n">sparse_weights</span><span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="n">_defaults</span><span class="o">.</span><span class="n">SPARSE_WEIGHTS</span><span class="p">,</span>
    <span class="n">enabled_precisions</span><span class="p">:</span> <span class="n">Union</span><span class="p">[</span>
        <span class="n">Set</span><span class="p">[</span><span class="n">Union</span><span class="p">[</span><span class="n">torch</span><span class="o">.</span><span class="n">dtype</span><span class="p">,</span> <span class="n">dtype</span><span class="p">]],</span> <span class="n">Tuple</span><span class="p">[</span><span class="n">Union</span><span class="p">[</span><span class="n">torch</span><span class="o">.</span><span class="n">dtype</span><span class="p">,</span> <span class="n">dtype</span><span class="p">]]</span>
    <span class="p">]</span> <span class="o">=</span> <span class="n">_defaults</span><span class="o">.</span><span class="n">ENABLED_PRECISIONS</span><span class="p">,</span>
    <span class="n">engine_capability</span><span class="p">:</span> <span class="n">EngineCapability</span> <span class="o">=</span> <span class="n">_defaults</span><span class="o">.</span><span class="n">ENGINE_CAPABILITY</span><span class="p">,</span>
    <span class="n">num_avg_timing_iters</span><span class="p">:</span> <span class="nb">int</span> <span class="o">=</span> <span class="n">_defaults</span><span class="o">.</span><span class="n">NUM_AVG_TIMING_ITERS</span><span class="p">,</span>
    <span class="n">workspace_size</span><span class="p">:</span> <span class="nb">int</span> <span class="o">=</span> <span class="n">_defaults</span><span class="o">.</span><span class="n">WORKSPACE_SIZE</span><span class="p">,</span>
    <span class="n">dla_sram_size</span><span class="p">:</span> <span class="nb">int</span> <span class="o">=</span> <span class="n">_defaults</span><span class="o">.</span><span class="n">DLA_SRAM_SIZE</span><span class="p">,</span>
    <span class="n">dla_local_dram_size</span><span class="p">:</span> <span class="nb">int</span> <span class="o">=</span> <span class="n">_defaults</span><span class="o">.</span><span class="n">DLA_LOCAL_DRAM_SIZE</span><span class="p">,</span>
    <span class="n">dla_global_dram_size</span><span class="p">:</span> <span class="nb">int</span> <span class="o">=</span> <span class="n">_defaults</span><span class="o">.</span><span class="n">DLA_GLOBAL_DRAM_SIZE</span><span class="p">,</span>
    <span class="n">truncate_double</span><span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="n">_defaults</span><span class="o">.</span><span class="n">TRUNCATE_DOUBLE</span><span class="p">,</span>
    <span class="n">require_full_compilation</span><span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="n">_defaults</span><span class="o">.</span><span class="n">REQUIRE_FULL_COMPILATION</span><span class="p">,</span>
    <span class="n">min_block_size</span><span class="p">:</span> <span class="nb">int</span> <span class="o">=</span> <span class="n">_defaults</span><span class="o">.</span><span class="n">MIN_BLOCK_SIZE</span><span class="p">,</span>
    <span class="n">torch_executed_ops</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="n">Collection</span><span class="p">[</span><span class="n">Target</span><span class="p">]]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
    <span class="n">torch_executed_modules</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="n">List</span><span class="p">[</span><span class="nb">str</span><span class="p">]]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
    <span class="n">pass_through_build_failures</span><span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="n">_defaults</span><span class="o">.</span><span class="n">PASS_THROUGH_BUILD_FAILURES</span><span class="p">,</span>
    <span class="n">max_aux_streams</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="nb">int</span><span class="p">]</span> <span class="o">=</span> <span class="n">_defaults</span><span class="o">.</span><span class="n">MAX_AUX_STREAMS</span><span class="p">,</span>
    <span class="n">version_compatible</span><span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="n">_defaults</span><span class="o">.</span><span class="n">VERSION_COMPATIBLE</span><span class="p">,</span>
    <span class="n">optimization_level</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="nb">int</span><span class="p">]</span> <span class="o">=</span> <span class="n">_defaults</span><span class="o">.</span><span class="n">OPTIMIZATION_LEVEL</span><span class="p">,</span>
    <span class="n">use_python_runtime</span><span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="n">_defaults</span><span class="o">.</span><span class="n">USE_PYTHON_RUNTIME</span><span class="p">,</span>
    <span class="n">use_fast_partitioner</span><span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="n">_defaults</span><span class="o">.</span><span class="n">USE_FAST_PARTITIONER</span><span class="p">,</span>
    <span class="n">enable_experimental_decompositions</span><span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="n">_defaults</span><span class="o">.</span><span class="n">ENABLE_EXPERIMENTAL_DECOMPOSITIONS</span><span class="p">,</span>
    <span class="n">dryrun</span><span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="n">_defaults</span><span class="o">.</span><span class="n">DRYRUN</span><span class="p">,</span>
    <span class="n">hardware_compatible</span><span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="n">_defaults</span><span class="o">.</span><span class="n">HARDWARE_COMPATIBLE</span><span class="p">,</span>
    <span class="n">timing_cache_path</span><span class="p">:</span> <span class="nb">str</span> <span class="o">=</span> <span class="n">_defaults</span><span class="o">.</span><span class="n">TIMING_CACHE_PATH</span><span class="p">,</span>
    <span class="n">lazy_engine_init</span><span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="n">_defaults</span><span class="o">.</span><span class="n">LAZY_ENGINE_INIT</span><span class="p">,</span>
    <span class="n">cache_built_engines</span><span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="n">_defaults</span><span class="o">.</span><span class="n">CACHE_BUILT_ENGINES</span><span class="p">,</span>
    <span class="n">reuse_cached_engines</span><span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="n">_defaults</span><span class="o">.</span><span class="n">REUSE_CACHED_ENGINES</span><span class="p">,</span>
    <span class="n">engine_cache_dir</span><span class="p">:</span> <span class="nb">str</span> <span class="o">=</span> <span class="n">_defaults</span><span class="o">.</span><span class="n">ENGINE_CACHE_DIR</span><span class="p">,</span>
    <span class="n">engine_cache_size</span><span class="p">:</span> <span class="nb">int</span> <span class="o">=</span> <span class="n">_defaults</span><span class="o">.</span><span class="n">ENGINE_CACHE_SIZE</span><span class="p">,</span>
    <span class="n">custom_engine_cache</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="n">BaseEngineCache</span><span class="p">]</span> <span class="o">=</span> <span class="n">_defaults</span><span class="o">.</span><span class="n">CUSTOM_ENGINE_CACHE</span><span class="p">,</span>
    <span class="n">use_explicit_typing</span><span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="n">_defaults</span><span class="o">.</span><span class="n">USE_EXPLICIT_TYPING</span><span class="p">,</span>
    <span class="n">use_fp32_acc</span><span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="n">_defaults</span><span class="o">.</span><span class="n">USE_FP32_ACC</span><span class="p">,</span>
    <span class="n">refit_identical_engine_weights</span><span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="n">_defaults</span><span class="o">.</span><span class="n">REFIT_IDENTICAL_ENGINE_WEIGHTS</span><span class="p">,</span>
    <span class="n">strip_engine_weights</span><span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="n">_defaults</span><span class="o">.</span><span class="n">STRIP_ENGINE_WEIGHTS</span><span class="p">,</span>
    <span class="n">immutable_weights</span><span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="n">_defaults</span><span class="o">.</span><span class="n">IMMUTABLE_WEIGHTS</span><span class="p">,</span>
    <span class="n">enable_weight_streaming</span><span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="n">_defaults</span><span class="o">.</span><span class="n">ENABLE_WEIGHT_STREAMING</span><span class="p">,</span>
    <span class="n">tiling_optimization_level</span><span class="p">:</span> <span class="nb">str</span> <span class="o">=</span> <span class="n">_defaults</span><span class="o">.</span><span class="n">TILING_OPTIMIZATION_LEVEL</span><span class="p">,</span>
    <span class="n">l2_limit_for_tiling</span><span class="p">:</span> <span class="nb">int</span> <span class="o">=</span> <span class="n">_defaults</span><span class="o">.</span><span class="n">L2_LIMIT_FOR_TILING</span><span class="p">,</span>
    <span class="n">offload_module_to_cpu</span><span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="n">_defaults</span><span class="o">.</span><span class="n">OFFLOAD_MODULE_TO_CPU</span><span class="p">,</span>
    <span class="n">use_distributed_mode_trace</span><span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="n">_defaults</span><span class="o">.</span><span class="n">USE_DISTRIBUTED_MODE_TRACE</span><span class="p">,</span>
    <span class="o">**</span><span class="n">kwargs</span><span class="p">:</span> <span class="n">Any</span><span class="p">,</span>
<span class="p">)</span> <span class="o">-&gt;</span> <span class="n">torch</span><span class="o">.</span><span class="n">fx</span><span class="o">.</span><span class="n">GraphModule</span><span class="p">:</span>
<span class="w">    </span><span class="sd">&quot;&quot;&quot;Compile an ExportedProgram module for NVIDIA GPUs using TensorRT</span>

<span class="sd">    Takes a existing TorchScript module and a set of settings to configure the compiler</span>
<span class="sd">    and will convert methods to JIT Graphs which call equivalent TensorRT engines</span>

<span class="sd">    Converts specifically the forward method of a TorchScript Module</span>

<span class="sd">    Arguments:</span>
<span class="sd">        exported_program (torch.export.ExportedProgram): Source module, running torch.export on a ``torch.nn.Module``</span>
<span class="sd">        inputs (Optional[Sequence[Sequence[Any]]]): List of specifications of input shape, dtype and memory layout for inputs to the module. This argument is required. Input Sizes can be specified as torch sizes, tuples or lists. dtypes can be specified using</span>
<span class="sd">            torch datatypes or torch_tensorrt datatypes and you can use either torch devices or the torch_tensorrt device type enum</span>
<span class="sd">            to select device type.</span>

<span class="sd">                .. code-block:: py</span>

<span class="sd">                    inputs=[</span>
<span class="sd">                        torch_tensorrt.Input((1, 3, 224, 224)), # Static NCHW input shape for input #1</span>
<span class="sd">                        torch_tensorrt.Input(</span>
<span class="sd">                            min_shape=(1, 224, 224, 3),</span>
<span class="sd">                            opt_shape=(1, 512, 512, 3),</span>
<span class="sd">                            max_shape=(1, 1024, 1024, 3),</span>
<span class="sd">                            dtype=torch.int32</span>
<span class="sd">                            format=torch.channel_last</span>
<span class="sd">                        ), # Dynamic input shape for input #2</span>
<span class="sd">                        torch.randn((1, 3, 224, 244)) # Use an example tensor and let torch_tensorrt infer settings</span>
<span class="sd">                    ]</span>

<span class="sd">    Keyword Arguments:</span>
<span class="sd">        arg_inputs (Optional[Sequence[Sequence[Any]]]): Same as inputs. Alias for better understanding with kwarg_inputs.</span>
<span class="sd">        kwarg_inputs (Optional[dict[Any, Any]]): kwarg inputs to the module forward function.</span>
<span class="sd">        device (Union(torch_tensorrt.Device, torch.device, dict)): Target device for TensorRT engines to run on ::</span>

<span class="sd">            device=torch_tensorrt.Device(&quot;dla:1&quot;, allow_gpu_fallback=True)</span>

<span class="sd">        disable_tf32 (bool): Force FP32 layers to use traditional as FP32 format vs the default behavior of rounding the inputs to 10-bit mantissas before multiplying, but accumulates the sum using 23-bit mantissas</span>
<span class="sd">        assume_dynamic_shape_support (bool): Setting this to true enables the converters work for both dynamic and static shapes. Default: False</span>
<span class="sd">        sparse_weights (bool): Enable sparsity for convolution and fully connected layers.</span>
<span class="sd">        enabled_precisions (Union[Set[Union[torch.dtype, dtype]], Tuple[Union[torch.dtype, dtype]]]): The set of datatypes that TensorRT can use when selecting kernels</span>
<span class="sd">        engine_capability (torch_tensorrt.EngineCapability): Restrict kernel selection to safe gpu kernels or safe dla kernels</span>
<span class="sd">        num_avg_timing_iters (int): Number of averaging timing iterations used to select kernels</span>
<span class="sd">        workspace_size (int): Maximum size of workspace given to TensorRT</span>
<span class="sd">        dla_sram_size (int): Fast software managed RAM used by DLA to communicate within a layer.</span>
<span class="sd">        dla_local_dram_size (int): Host RAM used by DLA to share intermediate tensor data across operations</span>
<span class="sd">        dla_global_dram_size (int): Host RAM used by DLA to store weights and metadata for execution</span>
<span class="sd">        truncate_double (bool): Truncate weights provided in double (float64) to float32</span>
<span class="sd">        require_full_compilation (bool): Require modules to be compiled end to end or return an error as opposed to returning a hybrid graph where operations that cannot be run in TensorRT are run in PyTorch</span>
<span class="sd">        min_block_size (int): The minimum number of contiguous TensorRT convertible operations in order to run a set of operations in TensorRT</span>
<span class="sd">        torch_executed_ops (Optional[Collection[Target]]): Set of aten operators that must be run in PyTorch. An error will be thrown if this set is not empty but ``require_full_compilation`` is True</span>
<span class="sd">        torch_executed_modules (Optional[List[str]]): List of modules that must be run in PyTorch. An error will be thrown if this list is not empty but ``require_full_compilation`` is True</span>
<span class="sd">        pass_through_build_failures (bool): Error out if there are issues during compilation (only applicable to torch.compile workflows)</span>
<span class="sd">        max_aux_streams (Optional[int]): Maximum streams in the engine</span>
<span class="sd">        version_compatible (bool): Build the TensorRT engines compatible with future versions of TensorRT (Restrict to lean runtime operators to provide version forward compatibility for the engines)</span>
<span class="sd">        optimization_level: (Optional[int]): Setting a higher optimization level allows TensorRT to spend longer engine building time searching for more optimization options. The resulting engine may have better performance compared to an engine built with a lower optimization level. The default optimization level is 3. Valid values include integers from 0 to the maximum optimization level, which is currently 5. Setting it to be greater than the maximum level results in identical behavior to the maximum level.</span>
<span class="sd">        use_python_runtime: (bool): Return a graph using a pure Python runtime, reduces options for serialization</span>
<span class="sd">        use_fast_partitioner: (bool): Use the adjacency based partitioning scheme instead of the global partitioner. Adjacency partitioning is faster but may not be optimal. Use the global paritioner (``False``) if looking for best performance</span>
<span class="sd">        enable_experimental_decompositions (bool): Use the full set of operator decompositions. These decompositions may not be tested but serve to make the graph easier to convert to TensorRT, potentially increasing the amount of graphs run in TensorRT.</span>
<span class="sd">        dryrun (bool): Toggle for &quot;Dryrun&quot; mode, running everything except conversion to TRT and logging outputs</span>
<span class="sd">        hardware_compatible (bool): Build the TensorRT engines compatible with GPU architectures other than that of the GPU on which the engine was built (currently works for NVIDIA Ampere and newer)</span>
<span class="sd">        timing_cache_path (str): Path to the timing cache if it exists (or) where it will be saved after compilation</span>
<span class="sd">        lazy_engine_init (bool): Defer setting up engines until the compilation of all engines is complete. Can allow larger models with multiple graph breaks to compile but can lead to oversubscription of GPU memory at runtime.</span>
<span class="sd">        cache_built_engines (bool): Whether to save the compiled TRT engines to storage</span>
<span class="sd">        reuse_cached_engines (bool): Whether to load the compiled TRT engines from storage</span>
<span class="sd">        engine_cache_dir (str): Directory to store the cached TRT engines</span>
<span class="sd">        engine_cache_size (int): Maximum hard-disk space (bytes) to use for the engine cache, default is 1GB. If the cache exceeds this size, the oldest engines will be removed by default</span>
<span class="sd">        custom_engine_cache (Optional[BaseEngineCache]): Engine cache instance to use for saving and loading engines. Users can provide their own engine cache by inheriting from BaseEngineCache. If used, engine_cache_dir and engine_cache_size will be ignored.</span>
<span class="sd">        use_explicit_typing (bool): This flag enables strong typing in TensorRT compilation which respects the precisions set in the Pytorch model. This is useful when users have mixed precision graphs.</span>
<span class="sd">        use_fp32_acc (bool): This option inserts cast to FP32 nodes around matmul layers and TensorRT ensures the accumulation of matmul happens in FP32. Use this only when FP16 precision is configured in enabled_precisions.</span>
<span class="sd">        refit_identical_engine_weights (bool): Refit engines with identical weights. This is useful when the same model is compiled multiple times with different inputs and the weights are the same. This will save time by reusing the same engine for different inputs.</span>
<span class="sd">        strip_engine_weights (bool): Strip engine weights from the serialized engine. This is useful when the engine is to be deployed in an environment where the weights are not required.</span>
<span class="sd">        immutable_weights (bool): Build non-refittable engines. This is useful for some layers that are not refittable. If this argument is set to true, `strip_engine_weights` and `refit_identical_engine_weights` will be ignored.</span>
<span class="sd">        enable_weight_streaming (bool): Enable weight streaming.</span>
<span class="sd">        tiling_optimization_level (str): The optimization level of tiling strategies. A higher level allows TensorRT to spend more time searching for better tiling strategy. We currently support [&quot;none&quot;, &quot;fast&quot;, &quot;moderate&quot;, &quot;full&quot;].</span>
<span class="sd">        l2_limit_for_tiling (int): The target L2 cache usage limit (in bytes) for tiling optimization (default is -1 which means no limit).</span>
<span class="sd">        offload_module_to_cpu (bool): Offload the module to CPU. This is useful when we need to minimize GPU memory usage.</span>
<span class="sd">        use_distributed_mode_trace (bool):  Using aot_autograd to trace the graph. This is enabled when DTensors or distributed tensors are present in distributed model</span>
<span class="sd">        **kwargs: Any,</span>
<span class="sd">    Returns:</span>
<span class="sd">        torch.fx.GraphModule: Compiled FX Module, when run it will execute via TensorRT</span>
<span class="sd">    &quot;&quot;&quot;</span>

    <span class="k">if</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s2">&quot;debug&quot;</span><span class="p">,</span> <span class="kc">False</span><span class="p">):</span>
        <span class="n">warnings</span><span class="o">.</span><span class="n">warn</span><span class="p">(</span>
            <span class="s2">&quot;`debug` is deprecated. Please use `with torch_tensorrt.dynamo.Debugger(...)` to wrap your compilation call to enable debugging functionality.&quot;</span><span class="p">,</span>
            <span class="ne">DeprecationWarning</span><span class="p">,</span>
            <span class="n">stacklevel</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span>
        <span class="p">)</span>

    <span class="k">if</span> <span class="s2">&quot;truncate_long_and_double&quot;</span> <span class="ow">in</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">keys</span><span class="p">():</span>
        <span class="k">if</span> <span class="n">truncate_double</span> <span class="ow">is</span> <span class="ow">not</span> <span class="n">_defaults</span><span class="o">.</span><span class="n">TRUNCATE_DOUBLE</span><span class="p">:</span>
            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span>
                <span class="s1">&#39;Provided configuration for &quot;truncate_double&quot; and deprecated API &quot;truncate_long_and_double&quot;, please only use &quot;truncate_double&quot;&#39;</span>
            <span class="p">)</span>
        <span class="k">else</span><span class="p">:</span>
            <span class="n">truncate_double</span> <span class="o">=</span> <span class="n">kwargs</span><span class="p">[</span><span class="s2">&quot;truncate_long_and_double&quot;</span><span class="p">]</span>
            <span class="n">warnings</span><span class="o">.</span><span class="n">warn</span><span class="p">(</span>
                <span class="s1">&#39;Compiler option &quot;truncate_long_and_double&quot; is deprecated in favor of &quot;truncate_double&quot; as int64 is now natively supported, this option will be removed in the next version&#39;</span><span class="p">,</span>
                <span class="ne">DeprecationWarning</span><span class="p">,</span>
                <span class="n">stacklevel</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span>
            <span class="p">)</span>

    <span class="k">if</span> <span class="s2">&quot;refit&quot;</span> <span class="ow">in</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">keys</span><span class="p">():</span>
        <span class="n">warnings</span><span class="o">.</span><span class="n">warn</span><span class="p">(</span>
            <span class="s2">&quot;`refit` is deprecated. Please set `immutable_weights=False` to build a refittable engine whose weights can be refitted&quot;</span><span class="p">,</span>
            <span class="ne">DeprecationWarning</span><span class="p">,</span>
            <span class="n">stacklevel</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span>
        <span class="p">)</span>
        <span class="k">if</span> <span class="n">immutable_weights</span><span class="p">:</span>
            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span>
                <span class="s2">&quot;Use flag `immutable_weights` only. Flag `refit` is deprecated.&quot;</span>
            <span class="p">)</span>
        <span class="k">else</span><span class="p">:</span>
            <span class="n">immutable_weights</span> <span class="o">=</span> <span class="ow">not</span> <span class="n">kwargs</span><span class="p">[</span><span class="s2">&quot;refit&quot;</span><span class="p">]</span>

    <span class="k">if</span> <span class="s2">&quot;make_refittable&quot;</span> <span class="ow">in</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">keys</span><span class="p">():</span>
        <span class="n">warnings</span><span class="o">.</span><span class="n">warn</span><span class="p">(</span>
            <span class="s2">&quot;`make_refittable` is deprecated. Please set `immutable_weights=False` to build a refittable engine whose weights can be refitted&quot;</span><span class="p">,</span>
            <span class="ne">DeprecationWarning</span><span class="p">,</span>
            <span class="n">stacklevel</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span>
        <span class="p">)</span>
        <span class="k">if</span> <span class="n">immutable_weights</span><span class="p">:</span>
            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span>
                <span class="s2">&quot;Use flag `immutable_weights` only. Flag `make_refittable` is deprecated.&quot;</span>
            <span class="p">)</span>
        <span class="k">else</span><span class="p">:</span>
            <span class="n">immutable_weights</span> <span class="o">=</span> <span class="ow">not</span> <span class="n">kwargs</span><span class="p">[</span><span class="s2">&quot;make_refittable&quot;</span><span class="p">]</span>

    <span class="k">if</span> <span class="n">refit_identical_engine_weights</span><span class="p">:</span>
        <span class="k">if</span> <span class="n">immutable_weights</span><span class="p">:</span>
            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span>
                <span class="s2">&quot;`immutable_weights` must be False when `refit_identical_engine_weights` is True.&quot;</span>
            <span class="p">)</span>

    <span class="k">if</span> <span class="p">(</span>
        <span class="s2">&quot;enable_cross_compile_for_windows&quot;</span> <span class="ow">in</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">keys</span><span class="p">()</span>
        <span class="ow">and</span> <span class="n">kwargs</span><span class="p">[</span><span class="s2">&quot;enable_cross_compile_for_windows&quot;</span><span class="p">]</span>
    <span class="p">):</span>
        <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span>
            <span class="s2">&quot;Please use torch_tensorrt.dynamo.cross_compile_for_windows() if you want to cross compile the module in Linux for inferencing in Windows.&quot;</span>
        <span class="p">)</span>

    <span class="n">engine_capability</span> <span class="o">=</span> <span class="n">EngineCapability</span><span class="o">.</span><span class="n">_from</span><span class="p">(</span><span class="n">engine_capability</span><span class="p">)</span>

    <span class="k">if</span> <span class="n">torch_executed_modules</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span> <span class="ow">and</span> <span class="n">torch_executed_modules</span><span class="p">:</span>
        <span class="n">logger</span><span class="o">.</span><span class="n">warning</span><span class="p">(</span>
            <span class="sa">f</span><span class="s2">&quot;Detected torch_executed_modules was non-empty: </span><span class="si">{</span><span class="n">torch_executed_modules</span><span class="si">}</span><span class="s2">&quot;</span>
            <span class="s2">&quot;</span><span class="se">\n</span><span class="s2">This feature is unimplemented in Torch-TRT Dynamo currently.&quot;</span>
        <span class="p">)</span>

    <span class="k">if</span> <span class="n">use_explicit_typing</span><span class="p">:</span>
        <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">enabled_precisions</span><span class="p">)</span> <span class="o">!=</span> <span class="mi">1</span> <span class="ow">or</span> <span class="ow">not</span> <span class="nb">any</span><span class="p">(</span>
            <span class="n">x</span> <span class="ow">in</span> <span class="n">enabled_precisions</span>
            <span class="k">for</span> <span class="n">x</span> <span class="ow">in</span> <span class="p">{</span><span class="n">torch</span><span class="o">.</span><span class="n">float32</span><span class="p">,</span> <span class="n">dtype</span><span class="o">.</span><span class="n">f32</span><span class="p">,</span> <span class="n">torch</span><span class="o">.</span><span class="n">float4_e2m1fn_x2</span><span class="p">,</span> <span class="n">dtype</span><span class="o">.</span><span class="n">f4</span><span class="p">}</span>
        <span class="p">):</span>
            <span class="k">raise</span> <span class="ne">AssertionError</span><span class="p">(</span>
                <span class="sa">f</span><span class="s2">&quot;use_explicit_typing was set to True, however found that enabled_precisions was also specified (saw: </span><span class="si">{</span><span class="n">enabled_precisions</span><span class="si">}</span><span class="s2">, expected: dtype.f32, dtype.f4). enabled_precisions should not be used when use_explicit_typing=True&quot;</span>
            <span class="p">)</span>

    <span class="k">if</span> <span class="n">use_fp32_acc</span><span class="p">:</span>
        <span class="n">logger</span><span class="o">.</span><span class="n">debug</span><span class="p">(</span>
            <span class="s2">&quot;FP32 accumulation for matmul layers is enabled. This option should only be enabled if the model already has FP16 weights and has no effect if it has FP32 weights. </span><span class="se">\</span>
<span class="s2">                     This flag inserts casts around matmul layers and ensures TensorRT executes the matmul layers in FP16 with FP32 accumulation.&quot;</span>
        <span class="p">)</span>

    <span class="k">if</span> <span class="n">enable_weight_streaming</span> <span class="ow">and</span> <span class="ow">not</span> <span class="n">use_explicit_typing</span><span class="p">:</span>
        <span class="k">raise</span> <span class="ne">AssertionError</span><span class="p">(</span>
            <span class="s2">&quot;When enable_weight_streaming is enabled, it requires use_explicit_typing to be set to True&quot;</span>
        <span class="p">)</span>
    <span class="c1"># Aliasing inputs to arg_inputs for better understanding</span>
    <span class="k">if</span> <span class="n">arg_inputs</span> <span class="ow">is</span> <span class="kc">None</span> <span class="ow">and</span> <span class="n">kwarg_inputs</span> <span class="ow">is</span> <span class="kc">None</span> <span class="ow">and</span> <span class="n">inputs</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
        <span class="k">raise</span> <span class="ne">AssertionError</span><span class="p">(</span>
            <span class="s2">&quot;&#39;arg_inputs&#39;, &#39;kwarg_inputs&#39; and &#39;inputs&#39; should not all be None.&quot;</span>
        <span class="p">)</span>

    <span class="k">elif</span> <span class="n">arg_inputs</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span> <span class="ow">and</span> <span class="n">inputs</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
        <span class="k">raise</span> <span class="ne">AssertionError</span><span class="p">(</span>
            <span class="s2">&quot;&#39;arg_inputs&#39; and &#39;inputs&#39; should not be used at the same time.&quot;</span>
        <span class="p">)</span>

    <span class="n">arg_inputs</span> <span class="o">=</span> <span class="n">inputs</span> <span class="ow">or</span> <span class="n">arg_inputs</span>

    <span class="k">if</span> <span class="n">kwarg_inputs</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
        <span class="n">kwarg_inputs</span> <span class="o">=</span> <span class="p">{}</span>

    <span class="k">if</span> <span class="ow">not</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">arg_inputs</span><span class="p">,</span> <span class="n">collections</span><span class="o">.</span><span class="n">abc</span><span class="o">.</span><span class="n">Sequence</span><span class="p">):</span>
        <span class="n">arg_inputs</span> <span class="o">=</span> <span class="p">[</span><span class="n">arg_inputs</span><span class="p">]</span>  <span class="c1"># type: ignore</span>

    <span class="c1"># Prepare torch_trt inputs</span>
    <span class="n">trt_arg_inputs</span><span class="p">:</span> <span class="n">Sequence</span><span class="p">[</span><span class="n">Input</span><span class="p">]</span> <span class="o">=</span> <span class="n">prepare_inputs</span><span class="p">(</span><span class="n">arg_inputs</span><span class="p">)</span>
    <span class="n">trt_kwarg_inputs</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="nb">dict</span><span class="p">[</span><span class="n">Any</span><span class="p">,</span> <span class="n">Any</span><span class="p">]]</span> <span class="o">=</span> <span class="n">prepare_inputs</span><span class="p">(</span><span class="n">kwarg_inputs</span><span class="p">)</span>
    <span class="n">device</span> <span class="o">=</span> <span class="n">to_torch_tensorrt_device</span><span class="p">(</span><span class="n">device</span><span class="p">)</span>
    <span class="n">enabled_precisions</span> <span class="o">=</span> <span class="p">{</span><span class="n">dtype</span><span class="o">.</span><span class="n">_from</span><span class="p">(</span><span class="n">p</span><span class="p">)</span> <span class="k">for</span> <span class="n">p</span> <span class="ow">in</span> <span class="n">enabled_precisions</span><span class="p">}</span>

    <span class="n">engine_cache</span> <span class="o">=</span> <span class="kc">None</span>
    <span class="k">if</span> <span class="n">cache_built_engines</span> <span class="ow">or</span> <span class="n">reuse_cached_engines</span><span class="p">:</span>
        <span class="n">engine_cache</span> <span class="o">=</span> <span class="p">(</span>
            <span class="n">custom_engine_cache</span>
            <span class="k">if</span> <span class="n">custom_engine_cache</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span>
            <span class="k">else</span> <span class="n">DiskEngineCache</span><span class="p">(</span><span class="n">engine_cache_dir</span><span class="p">,</span> <span class="n">engine_cache_size</span><span class="p">)</span>
        <span class="p">)</span>

    <span class="n">compilation_options</span> <span class="o">=</span> <span class="p">{</span>
        <span class="s2">&quot;enabled_precisions&quot;</span><span class="p">:</span> <span class="p">(</span>
            <span class="n">enabled_precisions</span> <span class="k">if</span> <span class="n">enabled_precisions</span> <span class="k">else</span> <span class="n">_defaults</span><span class="o">.</span><span class="n">ENABLED_PRECISIONS</span>
        <span class="p">),</span>
        <span class="s2">&quot;device&quot;</span><span class="p">:</span> <span class="n">device</span><span class="p">,</span>
        <span class="s2">&quot;assume_dynamic_shape_support&quot;</span><span class="p">:</span> <span class="n">assume_dynamic_shape_support</span><span class="p">,</span>
        <span class="s2">&quot;workspace_size&quot;</span><span class="p">:</span> <span class="n">workspace_size</span><span class="p">,</span>
        <span class="s2">&quot;min_block_size&quot;</span><span class="p">:</span> <span class="n">min_block_size</span><span class="p">,</span>
        <span class="s2">&quot;torch_executed_ops&quot;</span><span class="p">:</span> <span class="p">(</span>
            <span class="n">torch_executed_ops</span> <span class="k">if</span> <span class="n">torch_executed_ops</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span> <span class="k">else</span> <span class="nb">set</span><span class="p">()</span>
        <span class="p">),</span>
        <span class="s2">&quot;pass_through_build_failures&quot;</span><span class="p">:</span> <span class="n">pass_through_build_failures</span><span class="p">,</span>
        <span class="s2">&quot;max_aux_streams&quot;</span><span class="p">:</span> <span class="n">max_aux_streams</span><span class="p">,</span>
        <span class="s2">&quot;version_compatible&quot;</span><span class="p">:</span> <span class="n">version_compatible</span><span class="p">,</span>
        <span class="s2">&quot;optimization_level&quot;</span><span class="p">:</span> <span class="n">optimization_level</span><span class="p">,</span>
        <span class="s2">&quot;use_python_runtime&quot;</span><span class="p">:</span> <span class="n">use_python_runtime</span><span class="p">,</span>
        <span class="s2">&quot;truncate_double&quot;</span><span class="p">:</span> <span class="n">truncate_double</span><span class="p">,</span>
        <span class="s2">&quot;use_fast_partitioner&quot;</span><span class="p">:</span> <span class="n">use_fast_partitioner</span><span class="p">,</span>
        <span class="s2">&quot;num_avg_timing_iters&quot;</span><span class="p">:</span> <span class="n">num_avg_timing_iters</span><span class="p">,</span>
        <span class="s2">&quot;enable_experimental_decompositions&quot;</span><span class="p">:</span> <span class="n">enable_experimental_decompositions</span><span class="p">,</span>
        <span class="s2">&quot;require_full_compilation&quot;</span><span class="p">:</span> <span class="n">require_full_compilation</span><span class="p">,</span>
        <span class="s2">&quot;disable_tf32&quot;</span><span class="p">:</span> <span class="n">disable_tf32</span><span class="p">,</span>
        <span class="s2">&quot;sparse_weights&quot;</span><span class="p">:</span> <span class="n">sparse_weights</span><span class="p">,</span>
        <span class="s2">&quot;engine_capability&quot;</span><span class="p">:</span> <span class="n">engine_capability</span><span class="p">,</span>
        <span class="s2">&quot;dla_sram_size&quot;</span><span class="p">:</span> <span class="n">dla_sram_size</span><span class="p">,</span>
        <span class="s2">&quot;dla_local_dram_size&quot;</span><span class="p">:</span> <span class="n">dla_local_dram_size</span><span class="p">,</span>
        <span class="s2">&quot;dla_global_dram_size&quot;</span><span class="p">:</span> <span class="n">dla_global_dram_size</span><span class="p">,</span>
        <span class="s2">&quot;dryrun&quot;</span><span class="p">:</span> <span class="n">dryrun</span><span class="p">,</span>
        <span class="s2">&quot;hardware_compatible&quot;</span><span class="p">:</span> <span class="n">hardware_compatible</span><span class="p">,</span>
        <span class="s2">&quot;timing_cache_path&quot;</span><span class="p">:</span> <span class="n">timing_cache_path</span><span class="p">,</span>
        <span class="s2">&quot;lazy_engine_init&quot;</span><span class="p">:</span> <span class="n">lazy_engine_init</span><span class="p">,</span>
        <span class="s2">&quot;cache_built_engines&quot;</span><span class="p">:</span> <span class="n">cache_built_engines</span><span class="p">,</span>
        <span class="s2">&quot;reuse_cached_engines&quot;</span><span class="p">:</span> <span class="n">reuse_cached_engines</span><span class="p">,</span>
        <span class="s2">&quot;use_explicit_typing&quot;</span><span class="p">:</span> <span class="n">use_explicit_typing</span><span class="p">,</span>
        <span class="s2">&quot;use_fp32_acc&quot;</span><span class="p">:</span> <span class="n">use_fp32_acc</span><span class="p">,</span>
        <span class="s2">&quot;refit_identical_engine_weights&quot;</span><span class="p">:</span> <span class="n">refit_identical_engine_weights</span><span class="p">,</span>
        <span class="s2">&quot;strip_engine_weights&quot;</span><span class="p">:</span> <span class="n">strip_engine_weights</span><span class="p">,</span>
        <span class="s2">&quot;immutable_weights&quot;</span><span class="p">:</span> <span class="n">immutable_weights</span><span class="p">,</span>
        <span class="s2">&quot;enable_cross_compile_for_windows&quot;</span><span class="p">:</span> <span class="kc">False</span><span class="p">,</span>
        <span class="s2">&quot;enable_weight_streaming&quot;</span><span class="p">:</span> <span class="n">enable_weight_streaming</span><span class="p">,</span>
        <span class="s2">&quot;tiling_optimization_level&quot;</span><span class="p">:</span> <span class="n">tiling_optimization_level</span><span class="p">,</span>
        <span class="s2">&quot;l2_limit_for_tiling&quot;</span><span class="p">:</span> <span class="n">l2_limit_for_tiling</span><span class="p">,</span>
        <span class="s2">&quot;offload_module_to_cpu&quot;</span><span class="p">:</span> <span class="n">offload_module_to_cpu</span><span class="p">,</span>
        <span class="s2">&quot;use_distributed_mode_trace&quot;</span><span class="p">:</span> <span class="n">use_distributed_mode_trace</span><span class="p">,</span>
    <span class="p">}</span>
    <span class="n">logger</span><span class="o">.</span><span class="n">debug</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;CPU memory usage before lowering: </span><span class="si">{</span><span class="n">get_cpu_memory_usage</span><span class="p">()</span><span class="si">}</span><span class="s2"> MB&quot;</span><span class="p">)</span>
    <span class="n">settings</span> <span class="o">=</span> <span class="n">CompilationSettings</span><span class="p">(</span><span class="o">**</span><span class="n">compilation_options</span><span class="p">)</span>
    <span class="n">logger</span><span class="o">.</span><span class="n">info</span><span class="p">(</span><span class="s2">&quot;Compilation Settings: </span><span class="si">%s</span><span class="se">\n</span><span class="s2">&quot;</span><span class="p">,</span> <span class="n">settings</span><span class="p">)</span>
    <span class="n">exported_program</span> <span class="o">=</span> <span class="n">pre_export_lowering</span><span class="p">(</span><span class="n">exported_program</span><span class="p">,</span> <span class="n">settings</span><span class="p">)</span>
    <span class="n">exported_program</span> <span class="o">=</span> <span class="n">exported_program</span><span class="o">.</span><span class="n">run_decompositions</span><span class="p">(</span>
        <span class="n">get_decompositions</span><span class="p">(</span><span class="n">enable_experimental_decompositions</span><span class="p">)</span>
    <span class="p">)</span>

    <span class="n">gm</span> <span class="o">=</span> <span class="n">exported_program</span><span class="o">.</span><span class="n">module</span><span class="p">()</span>
    <span class="c1"># Move the weights in the state_dict to CPU</span>
    <span class="n">logger</span><span class="o">.</span><span class="n">debug</span><span class="p">(</span><span class="s2">&quot;Input graph: &quot;</span> <span class="o">+</span> <span class="nb">str</span><span class="p">(</span><span class="n">gm</span><span class="o">.</span><span class="n">graph</span><span class="p">))</span>

    <span class="c1"># Apply lowering on the graph module</span>
    <span class="n">gm</span> <span class="o">=</span> <span class="n">post_lowering</span><span class="p">(</span><span class="n">gm</span><span class="p">,</span> <span class="n">settings</span><span class="p">)</span>
    <span class="n">logger</span><span class="o">.</span><span class="n">debug</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;CPU memory usage after post_lowering: </span><span class="si">{</span><span class="n">get_cpu_memory_usage</span><span class="p">()</span><span class="si">}</span><span class="s2"> MB&quot;</span><span class="p">)</span>
    <span class="n">logger</span><span class="o">.</span><span class="n">debug</span><span class="p">(</span><span class="s2">&quot;Lowered Input graph: &quot;</span> <span class="o">+</span> <span class="nb">str</span><span class="p">(</span><span class="n">gm</span><span class="o">.</span><span class="n">graph</span><span class="p">))</span>

    <span class="c1"># Move the weights in the state_dict to CPU</span>
    <span class="k">if</span> <span class="n">offload_module_to_cpu</span><span class="p">:</span>
        <span class="n">deallocate_module</span><span class="p">(</span><span class="n">gm</span><span class="p">,</span> <span class="n">delete_module</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
        <span class="n">deallocate_module</span><span class="p">(</span><span class="n">exported_program</span><span class="o">.</span><span class="n">module</span><span class="p">(),</span> <span class="n">delete_module</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
        <span class="n">logger</span><span class="o">.</span><span class="n">info</span><span class="p">(</span>
            <span class="s2">&quot;The PyTorch model was moved to the CPU to allocate all GPU memory to TensorRT. To retain the model on the GPU, set offload_module_to_cpu=False&quot;</span>
        <span class="p">)</span>
        <span class="n">logger</span><span class="o">.</span><span class="n">debug</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;CPU memory usage after CPU offload: </span><span class="si">{</span><span class="n">get_cpu_memory_usage</span><span class="p">()</span><span class="si">}</span><span class="s2"> MB&quot;</span><span class="p">)</span>
    <span class="k">else</span><span class="p">:</span>
        <span class="n">remaining_memory</span><span class="p">,</span> <span class="n">total_memory</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">cuda</span><span class="o">.</span><span class="n">mem_get_info</span><span class="p">()</span>
        <span class="k">if</span> <span class="n">remaining_memory</span> <span class="o">&lt;</span> <span class="n">total_memory</span> <span class="o">//</span> <span class="mi">2</span><span class="p">:</span>
            <span class="n">logger</span><span class="o">.</span><span class="n">warning</span><span class="p">(</span>
                <span class="s2">&quot;Remaining GPU memory may not be enough to compile the TensorRT engine for this model resulting in an OOM error, Consider setting offload_module_to_cpu=True&quot;</span>
            <span class="p">)</span>
    <span class="n">trt_gm</span> <span class="o">=</span> <span class="n">compile_module</span><span class="p">(</span>
        <span class="n">gm</span><span class="p">,</span> <span class="n">trt_arg_inputs</span><span class="p">,</span> <span class="n">trt_kwarg_inputs</span><span class="p">,</span> <span class="n">settings</span><span class="p">,</span> <span class="n">engine_cache</span>
    <span class="p">)</span>
    <span class="k">return</span> <span class="n">trt_gm</span></div>


<span class="nd">@fn_supports_debugger</span>  <span class="c1"># type: ignore[misc]</span>
<span class="k">def</span><span class="w"> </span><span class="nf">compile_module</span><span class="p">(</span>
    <span class="n">gm</span><span class="p">:</span> <span class="n">torch</span><span class="o">.</span><span class="n">fx</span><span class="o">.</span><span class="n">GraphModule</span><span class="p">,</span>
    <span class="n">sample_arg_inputs</span><span class="p">:</span> <span class="n">Sequence</span><span class="p">[</span><span class="n">Input</span><span class="p">],</span>
    <span class="n">sample_kwarg_inputs</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="nb">dict</span><span class="p">[</span><span class="n">Any</span><span class="p">,</span> <span class="n">Any</span><span class="p">]]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
    <span class="n">settings</span><span class="p">:</span> <span class="n">CompilationSettings</span> <span class="o">=</span> <span class="n">CompilationSettings</span><span class="p">(),</span>
    <span class="n">engine_cache</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="n">BaseEngineCache</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
    <span class="o">*</span><span class="p">,</span>
    <span class="n">_debugger_config</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="n">DebuggerConfig</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
<span class="p">)</span> <span class="o">-&gt;</span> <span class="n">torch</span><span class="o">.</span><span class="n">fx</span><span class="o">.</span><span class="n">GraphModule</span><span class="p">:</span>
<span class="w">    </span><span class="sd">&quot;&quot;&quot;Compile a traced FX module</span>

<span class="sd">    Includes: Partitioning + Conversion Phases</span>

<span class="sd">    Args:</span>
<span class="sd">        module: FX GraphModule to convert</span>
<span class="sd">        arg_inputs: Inputs to the module</span>
<span class="sd">        kwarg_inputs: kwargs to the module</span>
<span class="sd">        settings: Compilation settings</span>
<span class="sd">        engine_cache: Engine cache instance to store/load compiled engines</span>
<span class="sd">    Returns:</span>
<span class="sd">        Compiled FX GraphModule</span>
<span class="sd">    &quot;&quot;&quot;</span>
    <span class="k">if</span> <span class="nb">any</span><span class="p">(</span><span class="n">v</span><span class="o">.</span><span class="n">requires_grad</span> <span class="k">for</span> <span class="n">v</span> <span class="ow">in</span> <span class="n">gm</span><span class="o">.</span><span class="n">state_dict</span><span class="p">()</span><span class="o">.</span><span class="n">values</span><span class="p">()):</span>
        <span class="n">logger</span><span class="o">.</span><span class="n">warning</span><span class="p">(</span>
            <span class="s2">&quot;The model may be in training mode, which may affect the performance of the compiled model!&quot;</span>
        <span class="p">)</span>
    <span class="n">dryrun_tracker</span> <span class="o">=</span> <span class="n">DryRunTracker</span><span class="p">()</span>
    <span class="k">if</span> <span class="n">sample_kwarg_inputs</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
        <span class="n">sample_kwarg_inputs</span> <span class="o">=</span> <span class="p">{}</span>

    <span class="c1"># Configure user compilation settings to converters.</span>
    <span class="n">CONVERTERS</span><span class="o">.</span><span class="n">set_compilation_settings</span><span class="p">(</span><span class="n">settings</span><span class="p">)</span>

    <span class="c1"># Check the number of supported operations in the graph</span>
    <span class="n">num_supported_ops</span><span class="p">,</span> <span class="n">total_ops</span> <span class="o">=</span> <span class="n">partitioning</span><span class="o">.</span><span class="n">get_graph_converter_support</span><span class="p">(</span>
        <span class="n">gm</span><span class="p">,</span> <span class="n">settings</span><span class="o">.</span><span class="n">torch_executed_ops</span>
    <span class="p">)</span>

    <span class="n">dryrun_tracker</span><span class="o">.</span><span class="n">total_ops_in_graph</span> <span class="o">=</span> <span class="n">total_ops</span>
    <span class="n">dryrun_tracker</span><span class="o">.</span><span class="n">supported_ops_in_graph</span> <span class="o">=</span> <span class="n">num_supported_ops</span>
    <span class="n">dryrun_tracker</span><span class="o">.</span><span class="n">compilation_settings</span> <span class="o">=</span> <span class="n">settings</span>

    <span class="k">if</span> <span class="n">settings</span><span class="o">.</span><span class="n">dryrun</span> <span class="ow">and</span> <span class="n">settings</span><span class="o">.</span><span class="n">min_block_size</span> <span class="o">&gt;</span> <span class="mi">1</span><span class="p">:</span>
        <span class="n">logger</span><span class="o">.</span><span class="n">info</span><span class="p">(</span>
            <span class="s2">&quot;It is recommended to run `dryrun` mode with `min_block_size=1`, &quot;</span>
            <span class="s2">&quot;for the most thorough analysis&quot;</span>
        <span class="p">)</span>

    <span class="c1"># If the number of supported operations is 0 or less than the block size, skip the subgraph</span>
    <span class="c1"># TODO: Add condition to second expression below when require_full_compilation is added</span>
    <span class="k">if</span> <span class="n">num_supported_ops</span> <span class="o">==</span> <span class="mi">0</span> <span class="ow">or</span> <span class="p">(</span>
        <span class="n">num_supported_ops</span> <span class="o">&lt;</span> <span class="n">settings</span><span class="o">.</span><span class="n">min_block_size</span> <span class="ow">and</span> <span class="ow">not</span> <span class="n">settings</span><span class="o">.</span><span class="n">dryrun</span>
    <span class="p">):</span>
        <span class="n">logger</span><span class="o">.</span><span class="n">warning</span><span class="p">(</span>
            <span class="sa">f</span><span class="s2">&quot;</span><span class="si">{</span><span class="n">num_supported_ops</span><span class="si">}</span><span class="s2"> supported operations detected in subgraph containing </span><span class="si">{</span><span class="n">total_ops</span><span class="si">}</span><span class="s2"> computational nodes. &quot;</span>
            <span class="sa">f</span><span class="s2">&quot;Skipping this subgraph, since min_block_size was detected to be </span><span class="si">{</span><span class="n">settings</span><span class="o">.</span><span class="n">min_block_size</span><span class="si">}</span><span class="s2">&quot;</span>
        <span class="p">)</span>
        <span class="k">return</span> <span class="n">gm</span>
    <span class="k">else</span><span class="p">:</span>
        <span class="n">logger</span><span class="o">.</span><span class="n">debug</span><span class="p">(</span>
            <span class="sa">f</span><span class="s2">&quot;Detected support for </span><span class="si">{</span><span class="n">num_supported_ops</span><span class="si">}</span><span class="s2"> operators out of </span><span class="si">{</span><span class="n">total_ops</span><span class="si">}</span><span class="s2"> in subgraph.&quot;</span>
        <span class="p">)</span>

    <span class="k">def</span><span class="w"> </span><span class="nf">contains_metadata</span><span class="p">(</span><span class="n">gm</span><span class="p">:</span> <span class="n">torch</span><span class="o">.</span><span class="n">fx</span><span class="o">.</span><span class="n">GraphModule</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">bool</span><span class="p">:</span>
        <span class="k">for</span> <span class="n">node</span> <span class="ow">in</span> <span class="n">gm</span><span class="o">.</span><span class="n">graph</span><span class="o">.</span><span class="n">nodes</span><span class="p">:</span>
            <span class="k">if</span> <span class="n">node</span><span class="o">.</span><span class="n">op</span> <span class="o">!=</span> <span class="s2">&quot;output&quot;</span> <span class="ow">and</span> <span class="p">(</span><span class="ow">not</span> <span class="n">node</span><span class="o">.</span><span class="n">meta</span><span class="p">)</span> <span class="ow">and</span> <span class="s2">&quot;val&quot;</span> <span class="ow">not</span> <span class="ow">in</span> <span class="n">node</span><span class="o">.</span><span class="n">meta</span><span class="p">:</span>
                <span class="n">logger</span><span class="o">.</span><span class="n">warning</span><span class="p">(</span>
                    <span class="sa">f</span><span class="s2">&quot;Node </span><span class="si">{</span><span class="n">node</span><span class="o">.</span><span class="n">name</span><span class="si">}</span><span class="s2"> of op type </span><span class="si">{</span><span class="n">node</span><span class="o">.</span><span class="n">op</span><span class="si">}</span><span class="s2"> does not have metadata. This could sometimes lead to undefined behavior.&quot;</span>
                <span class="p">)</span>
                <span class="k">return</span> <span class="kc">False</span>
        <span class="k">return</span> <span class="kc">True</span>

    <span class="c1"># Check if the module has metadata (shape, dtype).</span>
    <span class="k">if</span> <span class="ow">not</span> <span class="n">contains_metadata</span><span class="p">(</span><span class="n">gm</span><span class="p">):</span>
        <span class="c1"># TODO: For future, explore when nodes don&#39;t have metadata and if fake_tensor_prop can resolve this.</span>
        <span class="n">logger</span><span class="o">.</span><span class="n">warning</span><span class="p">(</span>
            <span class="s2">&quot;Some nodes do not have metadata (shape and dtype information). This could lead to problems sometimes if the graph has PyTorch and TensorRT segments.&quot;</span>
        <span class="p">)</span>

    <span class="c1"># Store the original input spec for later use</span>
    <span class="n">original_in_spec</span> <span class="o">=</span> <span class="nb">getattr</span><span class="p">(</span><span class="n">gm</span><span class="p">,</span> <span class="s2">&quot;_in_spec&quot;</span><span class="p">,</span> <span class="kc">None</span><span class="p">)</span>
    <span class="n">original_out_spec</span> <span class="o">=</span> <span class="nb">getattr</span><span class="p">(</span><span class="n">gm</span><span class="p">,</span> <span class="s2">&quot;_out_spec&quot;</span><span class="p">,</span> <span class="kc">None</span><span class="p">)</span>

    <span class="c1"># Function to preserve and restore module specs</span>
    <span class="k">def</span><span class="w"> </span><span class="nf">preserve_module_specs</span><span class="p">(</span>
        <span class="n">in_spec</span><span class="p">:</span> <span class="n">Any</span><span class="p">,</span> <span class="n">out_spec</span><span class="p">:</span> <span class="n">Any</span><span class="p">,</span> <span class="n">target_module</span><span class="p">:</span> <span class="n">torch</span><span class="o">.</span><span class="n">fx</span><span class="o">.</span><span class="n">GraphModule</span>
    <span class="p">)</span> <span class="o">-&gt;</span> <span class="kc">None</span><span class="p">:</span>
<span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd">        Applies input and output specs to the target module.</span>

<span class="sd">        Args:</span>
<span class="sd">            in_spec: The input spec to apply</span>
<span class="sd">            out_spec: The output spec to apply</span>
<span class="sd">            target_module: The module to apply specs to</span>
<span class="sd">        &quot;&quot;&quot;</span>
        <span class="c1"># Apply specs to target module</span>
        <span class="k">if</span> <span class="n">in_spec</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
            <span class="n">target_module</span><span class="o">.</span><span class="n">_in_spec</span> <span class="o">=</span> <span class="n">in_spec</span>
        <span class="k">if</span> <span class="n">out_spec</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
            <span class="n">target_module</span><span class="o">.</span><span class="n">_out_spec</span> <span class="o">=</span> <span class="n">out_spec</span>

    <span class="c1"># Partition module into components that can be TRT-accelerated</span>
    <span class="n">fast_partitioner_failed</span> <span class="o">=</span> <span class="kc">False</span>
    <span class="c1"># If specified, try using the fast partitioner and fall back to the global one on failure</span>
    <span class="k">if</span> <span class="n">settings</span><span class="o">.</span><span class="n">use_fast_partitioner</span><span class="p">:</span>
        <span class="k">try</span><span class="p">:</span>
            <span class="n">logger</span><span class="o">.</span><span class="n">info</span><span class="p">(</span><span class="s2">&quot;Partitioning the graph via the fast partitioner&quot;</span><span class="p">)</span>
            <span class="n">partitioned_module</span><span class="p">,</span> <span class="n">supported_ops</span> <span class="o">=</span> <span class="n">partitioning</span><span class="o">.</span><span class="n">fast_partition</span><span class="p">(</span>
                <span class="n">gm</span><span class="p">,</span>
                <span class="n">min_block_size</span><span class="o">=</span><span class="n">settings</span><span class="o">.</span><span class="n">min_block_size</span><span class="p">,</span>
                <span class="n">torch_executed_ops</span><span class="o">=</span><span class="n">settings</span><span class="o">.</span><span class="n">torch_executed_ops</span><span class="p">,</span>
                <span class="n">require_full_compilation</span><span class="o">=</span><span class="n">settings</span><span class="o">.</span><span class="n">require_full_compilation</span><span class="p">,</span>
                <span class="n">skip_fusion</span><span class="o">=</span><span class="p">(</span><span class="n">num_supported_ops</span> <span class="o">==</span> <span class="n">total_ops</span><span class="p">),</span>
            <span class="p">)</span>

        <span class="k">except</span> <span class="n">torch</span><span class="o">.</span><span class="n">fx</span><span class="o">.</span><span class="n">passes</span><span class="o">.</span><span class="n">splitter_base</span><span class="o">.</span><span class="n">FxNetSplitterInternalError</span><span class="p">:</span>
            <span class="n">logger</span><span class="o">.</span><span class="n">error</span><span class="p">(</span>
                <span class="s2">&quot;Partitioning failed on the subgraph with fast partition. See trace above. &quot;</span>
                <span class="s2">&quot;Retrying with global partition.&quot;</span><span class="p">,</span>
                <span class="n">exc_info</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
            <span class="p">)</span>

            <span class="n">fast_partitioner_failed</span> <span class="o">=</span> <span class="kc">True</span>
            <span class="n">settings</span><span class="o">.</span><span class="n">use_fast_partitioner</span> <span class="o">=</span> <span class="kc">False</span>

    <span class="k">if</span> <span class="ow">not</span> <span class="n">settings</span><span class="o">.</span><span class="n">use_fast_partitioner</span><span class="p">:</span>
        <span class="n">logger</span><span class="o">.</span><span class="n">info</span><span class="p">(</span><span class="s2">&quot;Partitioning the graph via the global partitioner&quot;</span><span class="p">)</span>
        <span class="n">partitioned_module</span><span class="p">,</span> <span class="n">supported_ops</span> <span class="o">=</span> <span class="n">partitioning</span><span class="o">.</span><span class="n">global_partition</span><span class="p">(</span>
            <span class="n">gm</span><span class="p">,</span>
            <span class="n">min_block_size</span><span class="o">=</span><span class="n">settings</span><span class="o">.</span><span class="n">min_block_size</span><span class="p">,</span>
            <span class="n">torch_executed_ops</span><span class="o">=</span><span class="n">settings</span><span class="o">.</span><span class="n">torch_executed_ops</span><span class="p">,</span>
            <span class="n">require_full_compilation</span><span class="o">=</span><span class="n">settings</span><span class="o">.</span><span class="n">require_full_compilation</span><span class="p">,</span>
        <span class="p">)</span>

    <span class="n">dryrun_tracker</span><span class="o">.</span><span class="n">unsupported_ops</span> <span class="o">=</span> <span class="n">supported_ops</span><span class="o">.</span><span class="n">unsupported_operators</span>

    <span class="c1"># The global partitioner leaves non-TRT nodes as-is</span>
    <span class="k">if</span> <span class="ow">not</span> <span class="n">settings</span><span class="o">.</span><span class="n">use_fast_partitioner</span><span class="p">:</span>
        <span class="n">dryrun_tracker</span><span class="o">.</span><span class="n">to_run_in_torch</span><span class="o">.</span><span class="n">extend</span><span class="p">(</span><span class="n">parse_non_trt_nodes</span><span class="p">(</span><span class="n">partitioned_module</span><span class="p">))</span>

    <span class="n">submodule_node_dict</span> <span class="o">=</span> <span class="p">{}</span>
    <span class="k">for</span> <span class="n">node</span> <span class="ow">in</span> <span class="n">partitioned_module</span><span class="o">.</span><span class="n">graph</span><span class="o">.</span><span class="n">nodes</span><span class="p">:</span>
        <span class="k">if</span> <span class="s2">&quot;_run_on_acc&quot;</span> <span class="ow">not</span> <span class="ow">in</span> <span class="n">node</span><span class="o">.</span><span class="n">name</span><span class="p">:</span>
            <span class="k">continue</span>
        <span class="n">submodule_node_dict</span><span class="p">[</span><span class="n">node</span><span class="o">.</span><span class="n">name</span><span class="p">]</span> <span class="o">=</span> <span class="n">node</span>

    <span class="n">preserve_module_specs</span><span class="p">(</span><span class="n">original_in_spec</span><span class="p">,</span> <span class="n">original_out_spec</span><span class="p">,</span> <span class="n">partitioned_module</span><span class="p">)</span>
    <span class="c1"># Store TRT replicas of Torch subgraphs</span>
    <span class="n">trt_modules</span> <span class="o">=</span> <span class="p">{}</span>
    <span class="c1"># Iterate over all components that can be accelerated</span>
    <span class="c1"># Generate the corresponding TRT Module for those</span>

    <span class="c1"># Here we delete the frozen parameters from the graph module. Note this does not affect the submodules. We are going to delete the frozen parameters from the submodules in the convert_module function.</span>
    <span class="c1"># This is done to release CPU memory.</span>
    <span class="k">for</span> <span class="n">attr</span> <span class="ow">in</span> <span class="nb">dir</span><span class="p">(</span><span class="n">gm</span><span class="p">):</span>
        <span class="k">if</span> <span class="n">attr</span><span class="o">.</span><span class="n">startswith</span><span class="p">(</span><span class="s2">&quot;_frozen_param&quot;</span><span class="p">):</span>
            <span class="nb">delattr</span><span class="p">(</span><span class="n">gm</span><span class="p">,</span> <span class="n">attr</span><span class="p">)</span>
    <span class="k">for</span> <span class="n">name</span><span class="p">,</span> <span class="n">_</span> <span class="ow">in</span> <span class="n">partitioned_module</span><span class="o">.</span><span class="n">named_children</span><span class="p">():</span>
        <span class="n">submodule</span> <span class="o">=</span> <span class="nb">getattr</span><span class="p">(</span><span class="n">partitioned_module</span><span class="p">,</span> <span class="n">name</span><span class="p">)</span>
        <span class="c1"># filter on the GraphModule</span>
        <span class="k">if</span> <span class="ow">not</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">submodule</span><span class="p">,</span> <span class="n">torch</span><span class="o">.</span><span class="n">fx</span><span class="o">.</span><span class="n">graph_module</span><span class="o">.</span><span class="n">GraphModule</span><span class="p">):</span>
            <span class="k">continue</span>
        <span class="c1"># Criteria for a module to be convertible to TRT</span>
        <span class="k">if</span> <span class="n">settings</span><span class="o">.</span><span class="n">use_fast_partitioner</span> <span class="ow">and</span> <span class="s2">&quot;_run_on_acc&quot;</span> <span class="ow">not</span> <span class="ow">in</span> <span class="n">name</span><span class="p">:</span>
            <span class="n">dryrun_tracker</span><span class="o">.</span><span class="n">to_run_in_torch</span><span class="o">.</span><span class="n">extend</span><span class="p">(</span><span class="n">parse_non_trt_nodes</span><span class="p">(</span><span class="n">submodule</span><span class="p">))</span>
            <span class="n">logger</span><span class="o">.</span><span class="n">debug</span><span class="p">(</span>
                <span class="s2">&quot;Submodule in PyTorch: </span><span class="si">%s</span><span class="se">\n</span><span class="s2"> </span><span class="si">%s</span><span class="s2">&quot;</span><span class="p">,</span>
                <span class="nb">str</span><span class="p">(</span><span class="n">name</span><span class="p">),</span>
                <span class="nb">str</span><span class="p">(</span><span class="n">submodule</span><span class="o">.</span><span class="n">graph</span><span class="p">),</span>
            <span class="p">)</span>
            <span class="n">submodule</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="n">to_torch_device</span><span class="p">(</span><span class="n">settings</span><span class="o">.</span><span class="n">device</span><span class="p">))</span>
            <span class="k">continue</span>

        <span class="k">if</span> <span class="n">name</span> <span class="ow">not</span> <span class="ow">in</span> <span class="n">submodule_node_dict</span><span class="p">:</span>
            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span>
                <span class="sa">f</span><span class="s2">&quot;node_name: </span><span class="si">{</span><span class="n">name</span><span class="si">}</span><span class="s2"> does not exist in the submodule node dictionary&quot;</span>
            <span class="p">)</span>

        <span class="c1"># set the submodule metadata back to the parent trt_module_node</span>
        <span class="n">metadata_list</span> <span class="o">=</span> <span class="n">get_output_metadata</span><span class="p">(</span><span class="n">submodule</span><span class="p">)</span>
        <span class="k">assert</span> <span class="nb">len</span><span class="p">(</span><span class="n">metadata_list</span><span class="p">)</span> <span class="o">&gt;</span> <span class="mi">0</span>
        <span class="n">metadata_keys</span> <span class="o">=</span> <span class="p">[</span><span class="s2">&quot;val&quot;</span><span class="p">,</span> <span class="s2">&quot;tensor_meta&quot;</span><span class="p">]</span>
        <span class="k">for</span> <span class="n">key</span> <span class="ow">in</span> <span class="n">metadata_keys</span><span class="p">:</span>
            <span class="k">if</span> <span class="n">key</span> <span class="ow">not</span> <span class="ow">in</span> <span class="n">submodule_node_dict</span><span class="p">[</span><span class="n">name</span><span class="p">]</span><span class="o">.</span><span class="n">meta</span><span class="p">:</span>
                <span class="n">meta_val_list</span> <span class="o">=</span> <span class="p">[</span>
                    <span class="n">metadata</span><span class="p">[</span><span class="n">key</span><span class="p">]</span> <span class="k">for</span> <span class="n">metadata</span> <span class="ow">in</span> <span class="n">metadata_list</span> <span class="k">if</span> <span class="n">key</span> <span class="ow">in</span> <span class="n">metadata</span>
                <span class="p">]</span>
                <span class="n">submodule_node_dict</span><span class="p">[</span><span class="n">name</span><span class="p">]</span><span class="o">.</span><span class="n">meta</span><span class="p">[</span><span class="n">key</span><span class="p">]</span> <span class="o">=</span> <span class="n">meta_val_list</span>
                <span class="n">logger</span><span class="o">.</span><span class="n">debug</span><span class="p">(</span>
                    <span class="sa">f</span><span class="s2">&quot;Updated metadata for node: </span><span class="si">{</span><span class="n">name</span><span class="si">}</span><span class="s2"> with its corresponding submodule outputs&quot;</span>
                <span class="p">)</span>
                <span class="k">break</span>

        <span class="n">subgraph_data</span> <span class="o">=</span> <span class="n">PerSubgraphData</span><span class="p">()</span>
        <span class="n">subgraph_data</span><span class="o">.</span><span class="n">subgraph_name</span> <span class="o">=</span> <span class="n">name</span>
        <span class="n">subgraph_data</span><span class="o">.</span><span class="n">subgraph_op_count</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span>
            <span class="p">[</span>
                <span class="n">node</span>
                <span class="k">for</span> <span class="n">node</span> <span class="ow">in</span> <span class="n">submodule</span><span class="o">.</span><span class="n">graph</span><span class="o">.</span><span class="n">nodes</span>
                <span class="k">if</span> <span class="n">node</span><span class="o">.</span><span class="n">op</span> <span class="ow">in</span> <span class="p">(</span><span class="s2">&quot;call_function&quot;</span><span class="p">,</span> <span class="s2">&quot;call_method&quot;</span><span class="p">,</span> <span class="s2">&quot;call_module&quot;</span><span class="p">)</span>
            <span class="p">]</span>
        <span class="p">)</span>

        <span class="c1"># Get the submodule inputs for min, opt, max shapes of the graph inputs</span>
        <span class="n">submodule_inputs</span> <span class="o">=</span> <span class="n">partitioning</span><span class="o">.</span><span class="n">construct_submodule_inputs</span><span class="p">(</span><span class="n">submodule</span><span class="p">)</span>

        <span class="k">assert</span> <span class="n">submodule_inputs</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span>

        <span class="n">logger</span><span class="o">.</span><span class="n">debug</span><span class="p">(</span>
            <span class="s2">&quot;Converting submodule: </span><span class="si">%s</span><span class="se">\n</span><span class="s2"> Input shapes: </span><span class="si">%s</span><span class="se">\n</span><span class="s2"> </span><span class="si">%s</span><span class="s2">&quot;</span><span class="p">,</span>
            <span class="nb">str</span><span class="p">(</span><span class="n">name</span><span class="p">),</span>
            <span class="p">[</span><span class="nb">input</span><span class="o">.</span><span class="n">shape</span> <span class="k">for</span> <span class="nb">input</span> <span class="ow">in</span> <span class="n">submodule_inputs</span><span class="p">],</span>
            <span class="nb">str</span><span class="p">(</span><span class="n">submodule</span><span class="o">.</span><span class="n">graph</span><span class="p">),</span>
        <span class="p">)</span>

        <span class="c1"># Handle long/double inputs if requested by the user</span>
        <span class="k">if</span> <span class="n">settings</span><span class="o">.</span><span class="n">truncate_double</span><span class="p">:</span>
            <span class="n">submodule_inputs</span> <span class="o">=</span> <span class="n">repair_double_inputs</span><span class="p">(</span>
                <span class="n">partitioned_module</span><span class="p">,</span>
                <span class="n">submodule</span><span class="p">,</span>
                <span class="n">submodule_inputs</span><span class="p">,</span>
                <span class="n">to_torch_device</span><span class="p">(</span><span class="n">settings</span><span class="o">.</span><span class="n">device</span><span class="p">),</span>
                <span class="n">name</span><span class="p">,</span>
            <span class="p">)</span>

        <span class="c1"># Parse the subgraph I/O and store it</span>
        <span class="n">parse_graph_io</span><span class="p">(</span><span class="n">submodule</span><span class="p">,</span> <span class="n">subgraph_data</span><span class="p">)</span>
        <span class="n">dryrun_tracker</span><span class="o">.</span><span class="n">tensorrt_graph_count</span> <span class="o">+=</span> <span class="mi">1</span>
        <span class="n">dryrun_tracker</span><span class="o">.</span><span class="n">per_subgraph_data</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">subgraph_data</span><span class="p">)</span>
        <span class="n">torch</span><span class="o">.</span><span class="n">cuda</span><span class="o">.</span><span class="n">empty_cache</span><span class="p">()</span>
        <span class="c1"># Create TRT engines from submodule</span>
        <span class="k">if</span> <span class="ow">not</span> <span class="n">settings</span><span class="o">.</span><span class="n">dryrun</span><span class="p">:</span>
            <span class="n">trt_module</span> <span class="o">=</span> <span class="n">convert_module</span><span class="p">(</span>
                <span class="n">submodule</span><span class="p">,</span>
                <span class="n">submodule_inputs</span><span class="p">,</span>
                <span class="n">settings</span><span class="o">=</span><span class="n">settings</span><span class="p">,</span>
                <span class="n">name</span><span class="o">=</span><span class="n">name</span><span class="p">,</span>
                <span class="n">engine_cache</span><span class="o">=</span><span class="n">engine_cache</span><span class="p">,</span>
            <span class="p">)</span>

            <span class="n">trt_modules</span><span class="p">[</span><span class="n">name</span><span class="p">]</span> <span class="o">=</span> <span class="n">trt_module</span>

            <span class="k">if</span> <span class="n">_debugger_config</span><span class="p">:</span>

                <span class="k">if</span> <span class="n">_debugger_config</span><span class="o">.</span><span class="n">save_engine_profile</span><span class="p">:</span>
                    <span class="k">if</span> <span class="n">settings</span><span class="o">.</span><span class="n">use_python_runtime</span><span class="p">:</span>
                        <span class="k">if</span> <span class="n">_debugger_config</span><span class="o">.</span><span class="n">profile_format</span> <span class="o">!=</span> <span class="s2">&quot;cudagraph&quot;</span><span class="p">:</span>
                            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span>
                                <span class="s2">&quot;Profiling with TREX can only be enabled when using the C++ runtime. Python runtime profiling only support cudagraph visualization.&quot;</span>
                            <span class="p">)</span>
                        <span class="k">else</span><span class="p">:</span>
                            <span class="n">trt_module</span><span class="o">.</span><span class="n">enable_profiling</span><span class="p">()</span>
                    <span class="k">else</span><span class="p">:</span>
                        <span class="k">if</span> <span class="n">_debugger_config</span><span class="o">.</span><span class="n">profile_format</span> <span class="o">==</span> <span class="s2">&quot;cudagraph&quot;</span><span class="p">:</span>
                            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span>
                                <span class="s2">&quot;Profiling with Cudagraph can only be enabled when using the Python runtime. C++ runtime profiling only support TREX/Perfetto visualization.&quot;</span>
                            <span class="p">)</span>
                        <span class="k">else</span><span class="p">:</span>
                            <span class="n">path</span> <span class="o">=</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span>
                                <span class="n">_debugger_config</span><span class="o">.</span><span class="n">logging_dir</span><span class="p">,</span>
                                <span class="s2">&quot;engine_visualization_profile&quot;</span><span class="p">,</span>
                            <span class="p">)</span>
                            <span class="n">os</span><span class="o">.</span><span class="n">makedirs</span><span class="p">(</span><span class="n">path</span><span class="p">,</span> <span class="n">exist_ok</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
                            <span class="n">trt_module</span><span class="o">.</span><span class="n">enable_profiling</span><span class="p">(</span>
                                <span class="n">profiling_results_dir</span><span class="o">=</span><span class="n">path</span><span class="p">,</span>
                                <span class="n">profile_format</span><span class="o">=</span><span class="n">_debugger_config</span><span class="o">.</span><span class="n">profile_format</span><span class="p">,</span>
                            <span class="p">)</span>

                <span class="k">if</span> <span class="n">_debugger_config</span><span class="o">.</span><span class="n">save_layer_info</span><span class="p">:</span>
                    <span class="k">with</span> <span class="nb">open</span><span class="p">(</span>
                        <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span>
                            <span class="n">_debugger_config</span><span class="o">.</span><span class="n">logging_dir</span><span class="p">,</span> <span class="s2">&quot;engine_layer_info.json&quot;</span>
                        <span class="p">),</span>
                        <span class="s2">&quot;w&quot;</span><span class="p">,</span>
                    <span class="p">)</span> <span class="k">as</span> <span class="n">f</span><span class="p">:</span>
                        <span class="n">f</span><span class="o">.</span><span class="n">write</span><span class="p">(</span><span class="n">trt_module</span><span class="o">.</span><span class="n">get_layer_info</span><span class="p">())</span>

    <span class="c1"># Parse the graph I/O and store it in dryrun tracker</span>
    <span class="n">parse_graph_io</span><span class="p">(</span><span class="n">gm</span><span class="p">,</span> <span class="n">dryrun_tracker</span><span class="p">)</span>

    <span class="c1"># Replace all FX Modules with TRT Modules</span>
    <span class="k">for</span> <span class="n">name</span><span class="p">,</span> <span class="n">trt_module</span> <span class="ow">in</span> <span class="n">trt_modules</span><span class="o">.</span><span class="n">items</span><span class="p">():</span>
        <span class="nb">setattr</span><span class="p">(</span><span class="n">partitioned_module</span><span class="p">,</span> <span class="n">name</span><span class="p">,</span> <span class="n">trt_module</span><span class="p">)</span>
        <span class="k">if</span> <span class="n">settings</span><span class="o">.</span><span class="n">lazy_engine_init</span> <span class="ow">and</span> <span class="ow">not</span> <span class="n">settings</span><span class="o">.</span><span class="n">enable_cross_compile_for_windows</span><span class="p">:</span>
            <span class="nb">getattr</span><span class="p">(</span><span class="n">partitioned_module</span><span class="p">,</span> <span class="n">name</span><span class="p">)</span><span class="o">.</span><span class="n">setup_engine</span><span class="p">()</span>

    <span class="c1"># Reset settings object to user specification after fallback to global partitioning mode</span>
    <span class="k">if</span> <span class="n">fast_partitioner_failed</span><span class="p">:</span>
        <span class="n">settings</span><span class="o">.</span><span class="n">use_fast_partitioner</span> <span class="o">=</span> <span class="kc">True</span>

    <span class="n">dryrun_stats_display</span><span class="p">(</span><span class="n">dryrun_tracker</span><span class="p">,</span> <span class="n">settings</span><span class="o">.</span><span class="n">dryrun</span><span class="p">)</span>

    <span class="k">return</span> <span class="n">partitioned_module</span>


<div class="viewcode-block" id="convert_exported_program_to_serialized_trt_engine"><a class="viewcode-back" href="../../../py_api/dynamo.html#torch_tensorrt.dynamo.convert_exported_program_to_serialized_trt_engine">[docs]</a><span class="k">def</span><span class="w"> </span><span class="nf">convert_exported_program_to_serialized_trt_engine</span><span class="p">(</span>
    <span class="n">exported_program</span><span class="p">:</span> <span class="n">ExportedProgram</span><span class="p">,</span>
    <span class="n">inputs</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="n">Sequence</span><span class="p">[</span><span class="n">Sequence</span><span class="p">[</span><span class="n">Any</span><span class="p">]]]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
    <span class="o">*</span><span class="p">,</span>
    <span class="n">arg_inputs</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="n">Sequence</span><span class="p">[</span><span class="n">Sequence</span><span class="p">[</span><span class="n">Any</span><span class="p">]]]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
    <span class="n">kwarg_inputs</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="nb">dict</span><span class="p">[</span><span class="n">Any</span><span class="p">,</span> <span class="n">Any</span><span class="p">]]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
    <span class="n">device</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="n">Union</span><span class="p">[</span><span class="n">Device</span><span class="p">,</span> <span class="n">torch</span><span class="o">.</span><span class="n">device</span><span class="p">,</span> <span class="nb">str</span><span class="p">]]</span> <span class="o">=</span> <span class="n">_defaults</span><span class="o">.</span><span class="n">DEVICE</span><span class="p">,</span>
    <span class="n">disable_tf32</span><span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="n">_defaults</span><span class="o">.</span><span class="n">DISABLE_TF32</span><span class="p">,</span>
    <span class="n">assume_dynamic_shape_support</span><span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="n">_defaults</span><span class="o">.</span><span class="n">ASSUME_DYNAMIC_SHAPE_SUPPORT</span><span class="p">,</span>
    <span class="n">sparse_weights</span><span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="n">_defaults</span><span class="o">.</span><span class="n">SPARSE_WEIGHTS</span><span class="p">,</span>
    <span class="n">enabled_precisions</span><span class="p">:</span> <span class="n">Union</span><span class="p">[</span>
        <span class="n">Set</span><span class="p">[</span><span class="n">Union</span><span class="p">[</span><span class="n">torch</span><span class="o">.</span><span class="n">dtype</span><span class="p">,</span> <span class="n">dtype</span><span class="p">]],</span> <span class="n">Tuple</span><span class="p">[</span><span class="n">Union</span><span class="p">[</span><span class="n">torch</span><span class="o">.</span><span class="n">dtype</span><span class="p">,</span> <span class="n">dtype</span><span class="p">]]</span>
    <span class="p">]</span> <span class="o">=</span> <span class="n">_defaults</span><span class="o">.</span><span class="n">ENABLED_PRECISIONS</span><span class="p">,</span>
    <span class="n">engine_capability</span><span class="p">:</span> <span class="n">EngineCapability</span> <span class="o">=</span> <span class="n">_defaults</span><span class="o">.</span><span class="n">ENGINE_CAPABILITY</span><span class="p">,</span>
    <span class="n">num_avg_timing_iters</span><span class="p">:</span> <span class="nb">int</span> <span class="o">=</span> <span class="n">_defaults</span><span class="o">.</span><span class="n">NUM_AVG_TIMING_ITERS</span><span class="p">,</span>
    <span class="n">workspace_size</span><span class="p">:</span> <span class="nb">int</span> <span class="o">=</span> <span class="n">_defaults</span><span class="o">.</span><span class="n">WORKSPACE_SIZE</span><span class="p">,</span>
    <span class="n">dla_sram_size</span><span class="p">:</span> <span class="nb">int</span> <span class="o">=</span> <span class="n">_defaults</span><span class="o">.</span><span class="n">DLA_SRAM_SIZE</span><span class="p">,</span>
    <span class="n">dla_local_dram_size</span><span class="p">:</span> <span class="nb">int</span> <span class="o">=</span> <span class="n">_defaults</span><span class="o">.</span><span class="n">DLA_LOCAL_DRAM_SIZE</span><span class="p">,</span>
    <span class="n">dla_global_dram_size</span><span class="p">:</span> <span class="nb">int</span> <span class="o">=</span> <span class="n">_defaults</span><span class="o">.</span><span class="n">DLA_GLOBAL_DRAM_SIZE</span><span class="p">,</span>
    <span class="n">truncate_double</span><span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="n">_defaults</span><span class="o">.</span><span class="n">TRUNCATE_DOUBLE</span><span class="p">,</span>
    <span class="n">require_full_compilation</span><span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="n">_defaults</span><span class="o">.</span><span class="n">REQUIRE_FULL_COMPILATION</span><span class="p">,</span>
    <span class="n">min_block_size</span><span class="p">:</span> <span class="nb">int</span> <span class="o">=</span> <span class="n">_defaults</span><span class="o">.</span><span class="n">MIN_BLOCK_SIZE</span><span class="p">,</span>
    <span class="n">torch_executed_ops</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="n">Collection</span><span class="p">[</span><span class="n">Target</span><span class="p">]]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
    <span class="n">torch_executed_modules</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="n">List</span><span class="p">[</span><span class="nb">str</span><span class="p">]]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
    <span class="n">pass_through_build_failures</span><span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="n">_defaults</span><span class="o">.</span><span class="n">PASS_THROUGH_BUILD_FAILURES</span><span class="p">,</span>
    <span class="n">max_aux_streams</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="nb">int</span><span class="p">]</span> <span class="o">=</span> <span class="n">_defaults</span><span class="o">.</span><span class="n">MAX_AUX_STREAMS</span><span class="p">,</span>
    <span class="n">version_compatible</span><span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="n">_defaults</span><span class="o">.</span><span class="n">VERSION_COMPATIBLE</span><span class="p">,</span>
    <span class="n">optimization_level</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="nb">int</span><span class="p">]</span> <span class="o">=</span> <span class="n">_defaults</span><span class="o">.</span><span class="n">OPTIMIZATION_LEVEL</span><span class="p">,</span>
    <span class="n">use_python_runtime</span><span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="n">_defaults</span><span class="o">.</span><span class="n">USE_PYTHON_RUNTIME</span><span class="p">,</span>
    <span class="n">use_fast_partitioner</span><span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="n">_defaults</span><span class="o">.</span><span class="n">USE_FAST_PARTITIONER</span><span class="p">,</span>
    <span class="n">enable_experimental_decompositions</span><span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="n">_defaults</span><span class="o">.</span><span class="n">ENABLE_EXPERIMENTAL_DECOMPOSITIONS</span><span class="p">,</span>
    <span class="n">dryrun</span><span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="n">_defaults</span><span class="o">.</span><span class="n">DRYRUN</span><span class="p">,</span>
    <span class="n">hardware_compatible</span><span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="n">_defaults</span><span class="o">.</span><span class="n">HARDWARE_COMPATIBLE</span><span class="p">,</span>
    <span class="n">timing_cache_path</span><span class="p">:</span> <span class="nb">str</span> <span class="o">=</span> <span class="n">_defaults</span><span class="o">.</span><span class="n">TIMING_CACHE_PATH</span><span class="p">,</span>
    <span class="n">lazy_engine_init</span><span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="n">_defaults</span><span class="o">.</span><span class="n">LAZY_ENGINE_INIT</span><span class="p">,</span>
    <span class="n">cache_built_engines</span><span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="n">_defaults</span><span class="o">.</span><span class="n">CACHE_BUILT_ENGINES</span><span class="p">,</span>
    <span class="n">reuse_cached_engines</span><span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="n">_defaults</span><span class="o">.</span><span class="n">REUSE_CACHED_ENGINES</span><span class="p">,</span>
    <span class="n">engine_cache_dir</span><span class="p">:</span> <span class="nb">str</span> <span class="o">=</span> <span class="n">_defaults</span><span class="o">.</span><span class="n">ENGINE_CACHE_DIR</span><span class="p">,</span>
    <span class="n">engine_cache_size</span><span class="p">:</span> <span class="nb">int</span> <span class="o">=</span> <span class="n">_defaults</span><span class="o">.</span><span class="n">ENGINE_CACHE_SIZE</span><span class="p">,</span>
    <span class="n">custom_engine_cache</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="n">BaseEngineCache</span><span class="p">]</span> <span class="o">=</span> <span class="n">_defaults</span><span class="o">.</span><span class="n">CUSTOM_ENGINE_CACHE</span><span class="p">,</span>
    <span class="n">use_explicit_typing</span><span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="n">_defaults</span><span class="o">.</span><span class="n">USE_EXPLICIT_TYPING</span><span class="p">,</span>
    <span class="n">use_fp32_acc</span><span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="n">_defaults</span><span class="o">.</span><span class="n">USE_FP32_ACC</span><span class="p">,</span>
    <span class="n">refit_identical_engine_weights</span><span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="n">_defaults</span><span class="o">.</span><span class="n">REFIT_IDENTICAL_ENGINE_WEIGHTS</span><span class="p">,</span>
    <span class="n">strip_engine_weights</span><span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="n">_defaults</span><span class="o">.</span><span class="n">STRIP_ENGINE_WEIGHTS</span><span class="p">,</span>
    <span class="n">immutable_weights</span><span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="n">_defaults</span><span class="o">.</span><span class="n">IMMUTABLE_WEIGHTS</span><span class="p">,</span>
    <span class="n">enable_weight_streaming</span><span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="n">_defaults</span><span class="o">.</span><span class="n">ENABLE_WEIGHT_STREAMING</span><span class="p">,</span>
    <span class="n">tiling_optimization_level</span><span class="p">:</span> <span class="nb">str</span> <span class="o">=</span> <span class="n">_defaults</span><span class="o">.</span><span class="n">TILING_OPTIMIZATION_LEVEL</span><span class="p">,</span>
    <span class="n">l2_limit_for_tiling</span><span class="p">:</span> <span class="nb">int</span> <span class="o">=</span> <span class="n">_defaults</span><span class="o">.</span><span class="n">L2_LIMIT_FOR_TILING</span><span class="p">,</span>
    <span class="n">offload_module_to_cpu</span><span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="n">_defaults</span><span class="o">.</span><span class="n">OFFLOAD_MODULE_TO_CPU</span><span class="p">,</span>
    <span class="n">use_distributed_mode_trace</span><span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="n">_defaults</span><span class="o">.</span><span class="n">USE_DISTRIBUTED_MODE_TRACE</span><span class="p">,</span>
    <span class="o">**</span><span class="n">kwargs</span><span class="p">:</span> <span class="n">Any</span><span class="p">,</span>
<span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">bytes</span><span class="p">:</span>
<span class="w">    </span><span class="sd">&quot;&quot;&quot;Convert an ExportedProgram to a serialized TensorRT engine</span>

<span class="sd">    Converts an ExportedProgram to a serialized TensorRT engine given a dictionary of conversion settings</span>

<span class="sd">    Arguments:</span>
<span class="sd">        exported_program (torch.export.ExportedProgram): Source module, running torch.export on a ``torch.nn.Module``</span>
<span class="sd">        inputs (Optional[Sequence[Sequence[Any]]]): List of specifications of input shape, dtype and memory layout for inputs to the module. This argument is required. Input Sizes can be specified as torch sizes, tuples or lists. dtypes can be specified using</span>
<span class="sd">            torch datatypes or torch_tensorrt datatypes and you can use either torch devices or the torch_tensorrt device type enum</span>
<span class="sd">            to select device type.</span>

<span class="sd">                .. code-block:: py</span>

<span class="sd">                    inputs=[</span>
<span class="sd">                        torch_tensorrt.Input((1, 3, 224, 224)), # Static NCHW input shape for input #1</span>
<span class="sd">                        torch_tensorrt.Input(</span>
<span class="sd">                            min_shape=(1, 224, 224, 3),</span>
<span class="sd">                            opt_shape=(1, 512, 512, 3),</span>
<span class="sd">                            max_shape=(1, 1024, 1024, 3),</span>
<span class="sd">                            dtype=torch.int32</span>
<span class="sd">                            format=torch.channel_last</span>
<span class="sd">                        ), # Dynamic input shape for input #2</span>
<span class="sd">                        torch.randn((1, 3, 224, 244)) # Use an example tensor and let torch_tensorrt infer settings</span>
<span class="sd">                    ]</span>

<span class="sd">    Keyword Arguments:</span>
<span class="sd">        arg_inputs (Optional[Sequence[Sequence[Any]]]): Same as inputs. Alias for better understanding with kwarg_inputs.</span>
<span class="sd">        kwarg_inputs (Optional[dict[Any, Any]]): kwarg inputs to the module forward function.</span>
<span class="sd">        device (Union(torch_tensorrt.Device, torch.device, dict)): Target device for TensorRT engines to run on ::</span>

<span class="sd">            device=torch_tensorrt.Device(&quot;dla:1&quot;, allow_gpu_fallback=True)</span>

<span class="sd">        disable_tf32 (bool): Force FP32 layers to use traditional as FP32 format vs the default behavior of rounding the inputs to 10-bit mantissas before multiplying, but accumulates the sum using 23-bit mantissas</span>
<span class="sd">        assume_dynamic_shape_support (bool): Setting this to true enables the converters work for both dynamic and static shapes. Default: False</span>
<span class="sd">        sparse_weights (bool): Enable sparsity for convolution and fully connected layers.</span>
<span class="sd">        enabled_precisions (Union[Set[Union[torch.dtype, dtype]], Tuple[Union[torch.dtype, dtype]]]): The set of datatypes that TensorRT can use when selecting kernels</span>
<span class="sd">        engine_capability (torch_tensorrt.EngineCapability): Restrict kernel selection to safe gpu kernels or safe dla kernels</span>
<span class="sd">        num_avg_timing_iters (int): Number of averaging timing iterations used to select kernels</span>
<span class="sd">        workspace_size (int): Maximum size of workspace given to TensorRT</span>
<span class="sd">        dla_sram_size (int): Fast software managed RAM used by DLA to communicate within a layer.</span>
<span class="sd">        dla_local_dram_size (int): Host RAM used by DLA to share intermediate tensor data across operations</span>
<span class="sd">        dla_global_dram_size (int): Host RAM used by DLA to store weights and metadata for execution</span>
<span class="sd">        truncate_double (bool): Truncate weights provided in double (float64) to float32</span>
<span class="sd">        require_full_compilation (bool): Require modules to be compiled end to end or return an error as opposed to returning a hybrid graph where operations that cannot be run in TensorRT are run in PyTorch</span>
<span class="sd">        min_block_size (int): The minimum number of contiguous TensorRT convertible operations in order to run a set of operations in TensorRT</span>
<span class="sd">        torch_executed_ops (Optional[Collection[Target]]): Set of aten operators that must be run in PyTorch. An error will be thrown if this set is not empty but ``require_full_compilation`` is True</span>
<span class="sd">        torch_executed_modules (Optional[List[str]]): List of modules that must be run in PyTorch. An error will be thrown if this list is not empty but ``require_full_compilation`` is True</span>
<span class="sd">        pass_through_build_failures (bool): Error out if there are issues during compilation (only applicable to torch.compile workflows)</span>
<span class="sd">        max_aux_streams (Optional[int]): Maximum streams in the engine</span>
<span class="sd">        version_compatible (bool): Build the TensorRT engines compatible with future versions of TensorRT (Restrict to lean runtime operators to provide version forward compatibility for the engines)</span>
<span class="sd">        optimization_level: (Optional[int]): Setting a higher optimization level allows TensorRT to spend longer engine building time searching for more optimization options. The resulting engine may have better performance compared to an engine built with a lower optimization level. The default optimization level is 3. Valid values include integers from 0 to the maximum optimization level, which is currently 5. Setting it to be greater than the maximum level results in identical behavior to the maximum level.</span>
<span class="sd">        use_python_runtime: (bool): Return a graph using a pure Python runtime, reduces options for serialization</span>
<span class="sd">        use_fast_partitioner: (bool): Use the adjacency based partitioning scheme instead of the global partitioner. Adjacency partitioning is faster but may not be optimal. Use the global paritioner (``False``) if looking for best performance</span>
<span class="sd">        enable_experimental_decompositions (bool): Use the full set of operator decompositions. These decompositions may not be tested but serve to make the graph easier to convert to TensorRT, potentially increasing the amount of graphs run in TensorRT.</span>
<span class="sd">        dryrun (bool): Toggle for &quot;Dryrun&quot; mode, running everything except conversion to TRT and logging outputs</span>
<span class="sd">        hardware_compatible (bool): Build the TensorRT engines compatible with GPU architectures other than that of the GPU on which the engine was built (currently works for NVIDIA Ampere and newer)</span>
<span class="sd">        timing_cache_path (str): Path to the timing cache if it exists (or) where it will be saved after compilation</span>
<span class="sd">        lazy_engine_init (bool): Defer setting up engines until the compilation of all engines is complete. Can allow larger models with multiple graph breaks to compile but can lead to oversubscription of GPU memory at runtime.</span>
<span class="sd">        cache_built_engines (bool): Whether to save the compiled TRT engines to storage</span>
<span class="sd">        reuse_cached_engines (bool): Whether to load the compiled TRT engines from storage</span>
<span class="sd">        engine_cache_dir (str): Directory to store the cached TRT engines</span>
<span class="sd">        engine_cache_size (int): Maximum hard-disk space (bytes) to use for the engine cache, default is 1GB. If the cache exceeds this size, the oldest engines will be removed by default</span>
<span class="sd">        custom_engine_cache (Optional[BaseEngineCache]): Engine cache instance to use for saving and loading engines. Users can provide their own engine cache by inheriting from BaseEngineCache. If used, engine_cache_dir and engine_cache_size will be ignored.</span>
<span class="sd">        use_explicit_typing (bool): This flag enables strong typing in TensorRT compilation which respects the precisions set in the Pytorch model. This is useful when users have mixed precision graphs.</span>
<span class="sd">        use_fp32_acc (bool): This option inserts cast to FP32 nodes around matmul layers and TensorRT ensures the accumulation of matmul happens in FP32. Use this only when FP16 precision is configured in enabled_precisions.</span>
<span class="sd">        refit_identical_engine_weights (bool): Refit engines with identical weights. This is useful when the same model is compiled multiple times with different inputs and the weights are the same. This will save time by reusing the same engine for different inputs.</span>
<span class="sd">        strip_engine_weights (bool): Strip engine weights from the serialized engine. This is useful when the engine is to be deployed in an environment where the weights are not required.</span>
<span class="sd">        immutable_weights (bool): Build non-refittable engines. This is useful for some layers that are not refittable. If this argument is set to true, `strip_engine_weights` and `refit_identical_engine_weights` will be ignored.</span>
<span class="sd">        enable_weight_streaming (bool): Enable weight streaming.</span>
<span class="sd">        tiling_optimization_level (str): The optimization level of tiling strategies. A higher level allows TensorRT to spend more time searching for better tiling strategy. We currently support [&quot;none&quot;, &quot;fast&quot;, &quot;moderate&quot;, &quot;full&quot;].</span>
<span class="sd">        l2_limit_for_tiling (int): The target L2 cache usage limit (in bytes) for tiling optimization (default is -1 which means no limit).</span>
<span class="sd">        offload_module_to_cpu (bool): Offload the module to CPU. This is useful when we need to minimize GPU memory usage.</span>
<span class="sd">        use_distributed_mode_trace (bool):  Using aot_autograd to trace the graph. This is enabled when DTensors or distributed tensors are present in distributed model.</span>
<span class="sd">        **kwargs: Any,</span>
<span class="sd">    Returns:</span>
<span class="sd">        bytes: Serialized TensorRT engine, can either be saved to a file or deserialized via TensorRT APIs</span>
<span class="sd">    &quot;&quot;&quot;</span>

    <span class="k">if</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s2">&quot;debug&quot;</span><span class="p">,</span> <span class="kc">False</span><span class="p">):</span>
        <span class="n">warnings</span><span class="o">.</span><span class="n">warn</span><span class="p">(</span>
            <span class="s2">&quot;`debug` is deprecated. Please use `with torch_tensorrt.dynamo.Debugger(...)` to wrap your compilation call to enable debugging functionality.&quot;</span><span class="p">,</span>
            <span class="ne">DeprecationWarning</span><span class="p">,</span>
            <span class="n">stacklevel</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span>
        <span class="p">)</span>

    <span class="k">if</span> <span class="s2">&quot;truncate_long_and_double&quot;</span> <span class="ow">in</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">keys</span><span class="p">():</span>
        <span class="k">if</span> <span class="n">truncate_double</span> <span class="ow">is</span> <span class="ow">not</span> <span class="n">_defaults</span><span class="o">.</span><span class="n">TRUNCATE_DOUBLE</span><span class="p">:</span>
            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span>
                <span class="s1">&#39;Provided configuration for &quot;truncate_double&quot; and deprecated API &quot;truncate_long_and_double&quot;, please only use &quot;truncate_double&quot;&#39;</span>
            <span class="p">)</span>
        <span class="k">else</span><span class="p">:</span>
            <span class="n">truncate_double</span> <span class="o">=</span> <span class="n">kwargs</span><span class="p">[</span><span class="s2">&quot;truncate_long_and_double&quot;</span><span class="p">]</span>
            <span class="n">warnings</span><span class="o">.</span><span class="n">warn</span><span class="p">(</span>
                <span class="s1">&#39;Compiler option &quot;truncate_long_and_double&quot; is deprecated in favor of &quot;truncate_double&quot; as int64 is now natively supported, this option will be removed in the next version&#39;</span><span class="p">,</span>
                <span class="ne">DeprecationWarning</span><span class="p">,</span>
                <span class="n">stacklevel</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span>
            <span class="p">)</span>

    <span class="k">if</span> <span class="s2">&quot;refit&quot;</span> <span class="ow">in</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">keys</span><span class="p">():</span>
        <span class="n">warnings</span><span class="o">.</span><span class="n">warn</span><span class="p">(</span>
            <span class="s2">&quot;`refit` is deprecated. Please set `immutable_weights=False` to build a refittable engine whose weights can be refitted&quot;</span><span class="p">,</span>
            <span class="ne">DeprecationWarning</span><span class="p">,</span>
            <span class="n">stacklevel</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span>
        <span class="p">)</span>
        <span class="k">if</span> <span class="n">immutable_weights</span><span class="p">:</span>
            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span>
                <span class="s2">&quot;Use flag `immutable_weights` only. Flag `refit` is deprecated.&quot;</span>
            <span class="p">)</span>
        <span class="k">else</span><span class="p">:</span>
            <span class="n">immutable_weights</span> <span class="o">=</span> <span class="ow">not</span> <span class="n">kwargs</span><span class="p">[</span><span class="s2">&quot;refit&quot;</span><span class="p">]</span>

    <span class="k">if</span> <span class="s2">&quot;make_refittable&quot;</span> <span class="ow">in</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">keys</span><span class="p">():</span>
        <span class="n">warnings</span><span class="o">.</span><span class="n">warn</span><span class="p">(</span>
            <span class="s2">&quot;`make_refittable` is deprecated. Please set `immutable_weights=False` to build a refittable engine whose weights can be refitted&quot;</span><span class="p">,</span>
            <span class="ne">DeprecationWarning</span><span class="p">,</span>
            <span class="n">stacklevel</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span>
        <span class="p">)</span>
        <span class="k">if</span> <span class="n">immutable_weights</span><span class="p">:</span>
            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span>
                <span class="s2">&quot;Use flag `immutable_weights` only. Flag `make_refittable` is deprecated.&quot;</span>
            <span class="p">)</span>
        <span class="k">else</span><span class="p">:</span>
            <span class="n">immutable_weights</span> <span class="o">=</span> <span class="ow">not</span> <span class="n">kwargs</span><span class="p">[</span><span class="s2">&quot;make_refittable&quot;</span><span class="p">]</span>

    <span class="k">if</span> <span class="n">refit_identical_engine_weights</span><span class="p">:</span>
        <span class="k">if</span> <span class="n">immutable_weights</span><span class="p">:</span>
            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span>
                <span class="s2">&quot;`immutable_weights` must be False when `refit_identical_engine_weights` is True.&quot;</span>
            <span class="p">)</span>

    <span class="k">if</span> <span class="p">(</span>
        <span class="s2">&quot;enable_cross_compile_for_windows&quot;</span> <span class="ow">in</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">keys</span><span class="p">()</span>
        <span class="ow">and</span> <span class="n">kwargs</span><span class="p">[</span><span class="s2">&quot;enable_cross_compile_for_windows&quot;</span><span class="p">]</span>
    <span class="p">):</span>
        <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span>
            <span class="s2">&quot;Please use torch_tensorrt.dynamo.cross_compile_for_windows() if you want to cross compile the module in Linux for inferencing in Windows.&quot;</span>
        <span class="p">)</span>

    <span class="n">engine_capability</span> <span class="o">=</span> <span class="n">EngineCapability</span><span class="o">.</span><span class="n">_from</span><span class="p">(</span><span class="n">engine_capability</span><span class="p">)</span>

    <span class="k">if</span> <span class="n">torch_executed_modules</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span> <span class="ow">and</span> <span class="n">torch_executed_modules</span><span class="p">:</span>
        <span class="n">logger</span><span class="o">.</span><span class="n">warning</span><span class="p">(</span>
            <span class="sa">f</span><span class="s2">&quot;Detected torch_executed_modules was non-empty: </span><span class="si">{</span><span class="n">torch_executed_modules</span><span class="si">}</span><span class="s2">&quot;</span>
            <span class="s2">&quot;</span><span class="se">\n</span><span class="s2">This feature is unimplemented in Torch-TRT Dynamo currently.&quot;</span>
        <span class="p">)</span>

    <span class="k">if</span> <span class="n">use_explicit_typing</span><span class="p">:</span>
        <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">enabled_precisions</span><span class="p">)</span> <span class="o">!=</span> <span class="mi">1</span> <span class="ow">or</span> <span class="ow">not</span> <span class="nb">any</span><span class="p">(</span>
            <span class="n">x</span> <span class="ow">in</span> <span class="n">enabled_precisions</span>
            <span class="k">for</span> <span class="n">x</span> <span class="ow">in</span> <span class="p">{</span><span class="n">torch</span><span class="o">.</span><span class="n">float32</span><span class="p">,</span> <span class="n">dtype</span><span class="o">.</span><span class="n">f32</span><span class="p">,</span> <span class="n">torch</span><span class="o">.</span><span class="n">float4_e2m1fn_x2</span><span class="p">,</span> <span class="n">dtype</span><span class="o">.</span><span class="n">f4</span><span class="p">}</span>
        <span class="p">):</span>
            <span class="k">raise</span> <span class="ne">AssertionError</span><span class="p">(</span>
                <span class="sa">f</span><span class="s2">&quot;use_explicit_typing was set to True, however found that enabled_precisions was also specified (saw: </span><span class="si">{</span><span class="n">enabled_precisions</span><span class="si">}</span><span class="s2">, expected: dtype.f32, dtype.f4). enabled_precisions should not be used when use_explicit_typing=True&quot;</span>
            <span class="p">)</span>

    <span class="k">if</span> <span class="n">use_fp32_acc</span><span class="p">:</span>
        <span class="n">logger</span><span class="o">.</span><span class="n">debug</span><span class="p">(</span>
            <span class="s2">&quot;FP32 accumulation for matmul layers is enabled. This option should only be enabled if the model already has FP16 weights and has no effect if it has FP32 weights. </span><span class="se">\</span>
<span class="s2">                     This flag inserts casts around matmul layers and ensures TensorRT executes the matmul layers in FP16 with FP32 accumulation.&quot;</span>
        <span class="p">)</span>

    <span class="k">if</span> <span class="n">enable_weight_streaming</span> <span class="ow">and</span> <span class="ow">not</span> <span class="n">use_explicit_typing</span><span class="p">:</span>
        <span class="k">raise</span> <span class="ne">AssertionError</span><span class="p">(</span>
            <span class="s2">&quot;When enable_weight_streaming is enabled, it requires use_explicit_typing to be set to True&quot;</span>
        <span class="p">)</span>
    <span class="c1"># Aliasing inputs to arg_inputs for better understanding</span>
    <span class="k">if</span> <span class="n">arg_inputs</span> <span class="ow">is</span> <span class="kc">None</span> <span class="ow">and</span> <span class="n">kwarg_inputs</span> <span class="ow">is</span> <span class="kc">None</span> <span class="ow">and</span> <span class="n">inputs</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
        <span class="k">raise</span> <span class="ne">AssertionError</span><span class="p">(</span>
            <span class="s2">&quot;&#39;arg_inputs&#39;, &#39;kwarg_inputs&#39; and &#39;inputs&#39; should not all be None.&quot;</span>
        <span class="p">)</span>

    <span class="k">elif</span> <span class="n">arg_inputs</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span> <span class="ow">and</span> <span class="n">inputs</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
        <span class="k">raise</span> <span class="ne">AssertionError</span><span class="p">(</span>
            <span class="s2">&quot;&#39;arg_inputs&#39; and &#39;inputs&#39; should not be used at the same time.&quot;</span>
        <span class="p">)</span>

    <span class="n">arg_inputs</span> <span class="o">=</span> <span class="n">inputs</span> <span class="ow">or</span> <span class="n">arg_inputs</span>

    <span class="k">if</span> <span class="n">kwarg_inputs</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
        <span class="n">kwarg_inputs</span> <span class="o">=</span> <span class="p">{}</span>

    <span class="k">if</span> <span class="ow">not</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">arg_inputs</span><span class="p">,</span> <span class="n">collections</span><span class="o">.</span><span class="n">abc</span><span class="o">.</span><span class="n">Sequence</span><span class="p">):</span>
        <span class="n">arg_inputs</span> <span class="o">=</span> <span class="p">[</span><span class="n">arg_inputs</span><span class="p">]</span>  <span class="c1"># type: ignore</span>

    <span class="c1"># Prepare torch_trt inputs</span>
    <span class="n">trt_arg_inputs</span><span class="p">:</span> <span class="n">Sequence</span><span class="p">[</span><span class="n">Input</span><span class="p">]</span> <span class="o">=</span> <span class="n">prepare_inputs</span><span class="p">(</span><span class="n">arg_inputs</span><span class="p">)</span>
    <span class="n">trt_kwarg_inputs</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="nb">dict</span><span class="p">[</span><span class="nb">str</span><span class="p">,</span> <span class="n">Any</span><span class="p">]]</span> <span class="o">=</span> <span class="n">prepare_inputs</span><span class="p">(</span><span class="n">kwarg_inputs</span><span class="p">)</span>
    <span class="n">device</span> <span class="o">=</span> <span class="n">to_torch_tensorrt_device</span><span class="p">(</span><span class="n">device</span><span class="p">)</span>
    <span class="n">enabled_precisions</span> <span class="o">=</span> <span class="p">{</span><span class="n">dtype</span><span class="o">.</span><span class="n">_from</span><span class="p">(</span><span class="n">p</span><span class="p">)</span> <span class="k">for</span> <span class="n">p</span> <span class="ow">in</span> <span class="n">enabled_precisions</span><span class="p">}</span>

    <span class="n">engine_cache</span> <span class="o">=</span> <span class="kc">None</span>
    <span class="k">if</span> <span class="n">cache_built_engines</span> <span class="ow">or</span> <span class="n">reuse_cached_engines</span><span class="p">:</span>
        <span class="n">engine_cache</span> <span class="o">=</span> <span class="p">(</span>
            <span class="n">custom_engine_cache</span>
            <span class="k">if</span> <span class="n">custom_engine_cache</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span>
            <span class="k">else</span> <span class="n">DiskEngineCache</span><span class="p">(</span><span class="n">engine_cache_dir</span><span class="p">,</span> <span class="n">engine_cache_size</span><span class="p">)</span>
        <span class="p">)</span>

    <span class="n">compilation_options</span> <span class="o">=</span> <span class="p">{</span>
        <span class="s2">&quot;enabled_precisions&quot;</span><span class="p">:</span> <span class="p">(</span>
            <span class="n">enabled_precisions</span> <span class="k">if</span> <span class="n">enabled_precisions</span> <span class="k">else</span> <span class="n">_defaults</span><span class="o">.</span><span class="n">ENABLED_PRECISIONS</span>
        <span class="p">),</span>
        <span class="s2">&quot;device&quot;</span><span class="p">:</span> <span class="n">device</span><span class="p">,</span>
        <span class="s2">&quot;assume_dynamic_shape_support&quot;</span><span class="p">:</span> <span class="n">assume_dynamic_shape_support</span><span class="p">,</span>
        <span class="s2">&quot;workspace_size&quot;</span><span class="p">:</span> <span class="n">workspace_size</span><span class="p">,</span>
        <span class="s2">&quot;min_block_size&quot;</span><span class="p">:</span> <span class="n">min_block_size</span><span class="p">,</span>
        <span class="s2">&quot;torch_executed_ops&quot;</span><span class="p">:</span> <span class="p">(</span>
            <span class="n">torch_executed_ops</span> <span class="k">if</span> <span class="n">torch_executed_ops</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span> <span class="k">else</span> <span class="nb">set</span><span class="p">()</span>
        <span class="p">),</span>
        <span class="s2">&quot;pass_through_build_failures&quot;</span><span class="p">:</span> <span class="n">pass_through_build_failures</span><span class="p">,</span>
        <span class="s2">&quot;max_aux_streams&quot;</span><span class="p">:</span> <span class="n">max_aux_streams</span><span class="p">,</span>
        <span class="s2">&quot;version_compatible&quot;</span><span class="p">:</span> <span class="n">version_compatible</span><span class="p">,</span>
        <span class="s2">&quot;optimization_level&quot;</span><span class="p">:</span> <span class="n">optimization_level</span><span class="p">,</span>
        <span class="s2">&quot;use_python_runtime&quot;</span><span class="p">:</span> <span class="n">use_python_runtime</span><span class="p">,</span>
        <span class="s2">&quot;truncate_double&quot;</span><span class="p">:</span> <span class="n">truncate_double</span><span class="p">,</span>
        <span class="s2">&quot;use_fast_partitioner&quot;</span><span class="p">:</span> <span class="n">use_fast_partitioner</span><span class="p">,</span>
        <span class="s2">&quot;num_avg_timing_iters&quot;</span><span class="p">:</span> <span class="n">num_avg_timing_iters</span><span class="p">,</span>
        <span class="s2">&quot;enable_experimental_decompositions&quot;</span><span class="p">:</span> <span class="n">enable_experimental_decompositions</span><span class="p">,</span>
        <span class="s2">&quot;require_full_compilation&quot;</span><span class="p">:</span> <span class="n">require_full_compilation</span><span class="p">,</span>
        <span class="s2">&quot;disable_tf32&quot;</span><span class="p">:</span> <span class="n">disable_tf32</span><span class="p">,</span>
        <span class="s2">&quot;sparse_weights&quot;</span><span class="p">:</span> <span class="n">sparse_weights</span><span class="p">,</span>
        <span class="s2">&quot;engine_capability&quot;</span><span class="p">:</span> <span class="n">engine_capability</span><span class="p">,</span>
        <span class="s2">&quot;dla_sram_size&quot;</span><span class="p">:</span> <span class="n">dla_sram_size</span><span class="p">,</span>
        <span class="s2">&quot;dla_local_dram_size&quot;</span><span class="p">:</span> <span class="n">dla_local_dram_size</span><span class="p">,</span>
        <span class="s2">&quot;dla_global_dram_size&quot;</span><span class="p">:</span> <span class="n">dla_global_dram_size</span><span class="p">,</span>
        <span class="s2">&quot;dryrun&quot;</span><span class="p">:</span> <span class="n">dryrun</span><span class="p">,</span>
        <span class="s2">&quot;hardware_compatible&quot;</span><span class="p">:</span> <span class="n">hardware_compatible</span><span class="p">,</span>
        <span class="s2">&quot;timing_cache_path&quot;</span><span class="p">:</span> <span class="n">timing_cache_path</span><span class="p">,</span>
        <span class="s2">&quot;lazy_engine_init&quot;</span><span class="p">:</span> <span class="n">lazy_engine_init</span><span class="p">,</span>
        <span class="s2">&quot;cache_built_engines&quot;</span><span class="p">:</span> <span class="n">cache_built_engines</span><span class="p">,</span>
        <span class="s2">&quot;reuse_cached_engines&quot;</span><span class="p">:</span> <span class="n">reuse_cached_engines</span><span class="p">,</span>
        <span class="s2">&quot;use_explicit_typing&quot;</span><span class="p">:</span> <span class="n">use_explicit_typing</span><span class="p">,</span>
        <span class="s2">&quot;use_fp32_acc&quot;</span><span class="p">:</span> <span class="n">use_fp32_acc</span><span class="p">,</span>
        <span class="s2">&quot;refit_identical_engine_weights&quot;</span><span class="p">:</span> <span class="n">refit_identical_engine_weights</span><span class="p">,</span>
        <span class="s2">&quot;strip_engine_weights&quot;</span><span class="p">:</span> <span class="n">strip_engine_weights</span><span class="p">,</span>
        <span class="s2">&quot;immutable_weights&quot;</span><span class="p">:</span> <span class="n">immutable_weights</span><span class="p">,</span>
        <span class="s2">&quot;enable_cross_compile_for_windows&quot;</span><span class="p">:</span> <span class="kc">False</span><span class="p">,</span>
        <span class="s2">&quot;enable_weight_streaming&quot;</span><span class="p">:</span> <span class="n">enable_weight_streaming</span><span class="p">,</span>
        <span class="s2">&quot;tiling_optimization_level&quot;</span><span class="p">:</span> <span class="n">tiling_optimization_level</span><span class="p">,</span>
        <span class="s2">&quot;l2_limit_for_tiling&quot;</span><span class="p">:</span> <span class="n">l2_limit_for_tiling</span><span class="p">,</span>
        <span class="s2">&quot;offload_module_to_cpu&quot;</span><span class="p">:</span> <span class="n">offload_module_to_cpu</span><span class="p">,</span>
        <span class="s2">&quot;use_distributed_mode_trace&quot;</span><span class="p">:</span> <span class="n">use_distributed_mode_trace</span><span class="p">,</span>
    <span class="p">}</span>

    <span class="n">settings</span> <span class="o">=</span> <span class="n">CompilationSettings</span><span class="p">(</span><span class="o">**</span><span class="n">compilation_options</span><span class="p">)</span>
    <span class="n">logger</span><span class="o">.</span><span class="n">info</span><span class="p">(</span><span class="s2">&quot;Compilation Settings: </span><span class="si">%s</span><span class="se">\n</span><span class="s2">&quot;</span><span class="p">,</span> <span class="n">settings</span><span class="p">)</span>
    <span class="n">exported_program</span> <span class="o">=</span> <span class="n">pre_export_lowering</span><span class="p">(</span><span class="n">exported_program</span><span class="p">,</span> <span class="n">settings</span><span class="p">)</span>
    <span class="n">exported_program</span> <span class="o">=</span> <span class="n">exported_program</span><span class="o">.</span><span class="n">run_decompositions</span><span class="p">(</span>
        <span class="n">get_decompositions</span><span class="p">(</span><span class="n">enable_experimental_decompositions</span><span class="p">)</span>
    <span class="p">)</span>

    <span class="n">gm</span> <span class="o">=</span> <span class="n">exported_program</span><span class="o">.</span><span class="n">module</span><span class="p">()</span>
    <span class="c1"># Move the weights in the state_dict to CPU</span>
    <span class="n">logger</span><span class="o">.</span><span class="n">debug</span><span class="p">(</span><span class="s2">&quot;Input graph: &quot;</span> <span class="o">+</span> <span class="nb">str</span><span class="p">(</span><span class="n">gm</span><span class="o">.</span><span class="n">graph</span><span class="p">))</span>

    <span class="c1"># Apply lowering on the graph module</span>
    <span class="n">gm</span> <span class="o">=</span> <span class="n">post_lowering</span><span class="p">(</span><span class="n">gm</span><span class="p">,</span> <span class="n">settings</span><span class="p">)</span>
    <span class="n">logger</span><span class="o">.</span><span class="n">debug</span><span class="p">(</span><span class="s2">&quot;Lowered Input graph: &quot;</span> <span class="o">+</span> <span class="nb">str</span><span class="p">(</span><span class="n">gm</span><span class="o">.</span><span class="n">graph</span><span class="p">))</span>

    <span class="c1"># Move the weights in the state_dict to CPU</span>
    <span class="k">if</span> <span class="n">offload_module_to_cpu</span><span class="p">:</span>
        <span class="n">deallocate_module</span><span class="p">(</span><span class="n">exported_program</span><span class="o">.</span><span class="n">module</span><span class="p">(),</span> <span class="n">delete_module</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
        <span class="n">logger</span><span class="o">.</span><span class="n">info</span><span class="p">(</span>
            <span class="s2">&quot;The PyTorch model was moved to the CPU to allocate all GPU memory to TensorRT. To retain the model on the GPU, set offload_module_to_cpu=False&quot;</span>
        <span class="p">)</span>
    <span class="k">else</span><span class="p">:</span>
        <span class="n">remaining_memory</span><span class="p">,</span> <span class="n">total_memory</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">cuda</span><span class="o">.</span><span class="n">mem_get_info</span><span class="p">()</span>
        <span class="k">if</span> <span class="n">remaining_memory</span> <span class="o">&lt;</span> <span class="n">total_memory</span> <span class="o">//</span> <span class="mi">2</span><span class="p">:</span>
            <span class="n">logger</span><span class="o">.</span><span class="n">warning</span><span class="p">(</span>
                <span class="s2">&quot;Remaining GPU memory may not be enough to compile the TensorRT engine for this model resulting in an OOM error, Consider setting offload_module_to_cpu=True&quot;</span>
            <span class="p">)</span>

    <span class="n">flattened_input_list</span> <span class="o">=</span> <span class="n">get_flat_args_with_check</span><span class="p">(</span>
        <span class="n">exported_program</span><span class="p">,</span> <span class="nb">list</span><span class="p">(</span><span class="n">trt_arg_inputs</span><span class="p">),</span> <span class="n">trt_kwarg_inputs</span>  <span class="c1"># type: ignore</span>
    <span class="p">)[</span><span class="mi">0</span><span class="p">]</span>

    <span class="k">try</span><span class="p">:</span>
        <span class="n">interpreter_result</span> <span class="o">=</span> <span class="n">interpret_module_to_result</span><span class="p">(</span>
            <span class="n">gm</span><span class="p">,</span>
            <span class="n">inputs</span><span class="o">=</span><span class="n">flattened_input_list</span><span class="p">,</span>
            <span class="n">arg_inputs</span><span class="o">=</span><span class="nb">list</span><span class="p">(</span><span class="n">trt_arg_inputs</span><span class="p">),</span>
            <span class="n">kwarg_inputs</span><span class="o">=</span><span class="n">trt_kwarg_inputs</span><span class="p">,</span>
            <span class="n">settings</span><span class="o">=</span><span class="n">settings</span><span class="p">,</span>
            <span class="n">engine_cache</span><span class="o">=</span><span class="n">engine_cache</span><span class="p">,</span>
        <span class="p">)</span>
    <span class="k">except</span> <span class="n">UnsupportedOperatorException</span><span class="p">:</span>
        <span class="n">logger</span><span class="o">.</span><span class="n">error</span><span class="p">(</span>
            <span class="sa">f</span><span class="s2">&quot;Conversion of module </span><span class="si">{</span><span class="n">gm</span><span class="si">}</span><span class="s2"> not currently fully supported or convertible!&quot;</span><span class="p">,</span>
            <span class="n">exc_info</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
        <span class="p">)</span>
    <span class="k">except</span> <span class="ne">Exception</span> <span class="k">as</span> <span class="n">e</span><span class="p">:</span>
        <span class="n">logger</span><span class="o">.</span><span class="n">error</span><span class="p">(</span>
            <span class="sa">f</span><span class="s2">&quot;While interpreting the module got an error: </span><span class="si">{</span><span class="n">e</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">,</span>
            <span class="n">exc_info</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
        <span class="p">)</span>

    <span class="n">serialized_engine</span><span class="p">:</span> <span class="nb">bytes</span> <span class="o">=</span> <span class="n">interpreter_result</span><span class="o">.</span><span class="n">serialized_engine</span>
    <span class="k">return</span> <span class="n">serialized_engine</span></div>


<span class="nd">@needs_cross_compile</span>  <span class="c1"># type: ignore[misc]</span>
<span class="k">def</span><span class="w"> </span><span class="nf">save_cross_compiled_exported_program</span><span class="p">(</span>
    <span class="n">gm</span><span class="p">:</span> <span class="n">torch</span><span class="o">.</span><span class="n">fx</span><span class="o">.</span><span class="n">GraphModule</span><span class="p">,</span>
    <span class="n">file_path</span><span class="p">:</span> <span class="nb">str</span><span class="p">,</span>
<span class="p">)</span> <span class="o">-&gt;</span> <span class="kc">None</span><span class="p">:</span>
<span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd">    Save cross compiled exported program to disk.</span>

<span class="sd">    Arguments:</span>
<span class="sd">        module (torch.fx.GraphModule): Cross compiled Torch-TensorRT module</span>
<span class="sd">        file_path (str): the file path where the exported program will be saved to disk</span>
<span class="sd">    &quot;&quot;&quot;</span>
    <span class="k">if</span> <span class="ow">not</span> <span class="n">file_path</span><span class="p">:</span>
        <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">&quot;File path cannot be empty. Please provide a valid file path&quot;</span><span class="p">)</span>

    <span class="kn">from</span><span class="w"> </span><span class="nn">torch_tensorrt.dynamo._exporter</span><span class="w"> </span><span class="kn">import</span> <span class="n">export</span>

    <span class="n">exp_program</span> <span class="o">=</span> <span class="n">export</span><span class="p">(</span><span class="n">gm</span><span class="p">,</span> <span class="n">cross_compile_module</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
    <span class="n">torch</span><span class="o">.</span><span class="n">export</span><span class="o">.</span><span class="n">save</span><span class="p">(</span><span class="n">exp_program</span><span class="p">,</span> <span class="n">file_path</span><span class="p">)</span>
    <span class="n">logger</span><span class="o">.</span><span class="n">debug</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;successfully saved the module for windows at </span><span class="si">{</span><span class="n">file_path</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span>


<div class="viewcode-block" id="load_cross_compiled_exported_program"><a class="viewcode-back" href="../../../py_api/dynamo.html#torch_tensorrt.dynamo.load_cross_compiled_exported_program">[docs]</a><span class="k">def</span><span class="w"> </span><span class="nf">load_cross_compiled_exported_program</span><span class="p">(</span><span class="n">file_path</span><span class="p">:</span> <span class="nb">str</span> <span class="o">=</span> <span class="s2">&quot;&quot;</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">Any</span><span class="p">:</span>
<span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd">    Load an ExportedProgram file in Windows which was previously cross compiled in Linux</span>

<span class="sd">    Arguments:</span>
<span class="sd">        file_path (str): Path to file on the disk</span>

<span class="sd">    Raises:</span>
<span class="sd">        ValueError: If the api is not called in windows or there is no file or the file is a valid ExportedProgram file</span>
<span class="sd">    &quot;&quot;&quot;</span>
    <span class="k">if</span> <span class="ow">not</span> <span class="n">file_path</span><span class="p">:</span>
        <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">&quot;File path cannot be empty. Please provide a valid file path&quot;</span><span class="p">)</span>

    <span class="k">if</span> <span class="n">platform</span><span class="o">.</span><span class="n">system</span><span class="p">()</span> <span class="o">!=</span> <span class="s2">&quot;Windows&quot;</span> <span class="ow">or</span> <span class="n">platform</span><span class="o">.</span><span class="n">machine</span><span class="p">()</span> <span class="o">!=</span> <span class="s2">&quot;AMD64&quot;</span><span class="p">:</span>
        <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span>
            <span class="s2">&quot;cross runtime compiled model for windows can only be loaded in Windows system&quot;</span>
        <span class="p">)</span>

    <span class="k">try</span><span class="p">:</span>
        <span class="n">logger</span><span class="o">.</span><span class="n">debug</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;Loading the provided file </span><span class="si">{</span><span class="n">file_path</span><span class="si">}</span><span class="s2"> using torch.export.load()&quot;</span><span class="p">)</span>
        <span class="c1"># TODO: think about how to handle the torch.jit.load route?</span>
        <span class="n">exp_program</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">export</span><span class="o">.</span><span class="n">load</span><span class="p">(</span><span class="n">file_path</span><span class="p">)</span>
    <span class="k">except</span> <span class="ne">Exception</span> <span class="k">as</span> <span class="n">e</span><span class="p">:</span>
        <span class="n">logger</span><span class="o">.</span><span class="n">info</span><span class="p">(</span>
            <span class="sa">f</span><span class="s2">&quot;Loading the provided file </span><span class="si">{</span><span class="n">file_path</span><span class="si">}</span><span class="s2"> via torch.export.load() failed with the following error: </span><span class="si">{</span><span class="n">e</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">,</span>
            <span class="n">exc_info</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
        <span class="p">)</span>
        <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span>
            <span class="sa">f</span><span class="s2">&quot;cross_load the file </span><span class="si">{</span><span class="n">file_path</span><span class="si">}</span><span class="s2"> doesn&#39;t correspond to a valid ExportedProgram. Please verify the file path.&quot;</span>
        <span class="p">)</span>

    <span class="k">return</span> <span class="n">replace_execute_engine_no_op_node</span><span class="p">(</span><span class="n">exp_program</span><span class="p">)</span></div>
</pre></div>

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